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@@ -53,3 +53,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+data/job_trace/trace_previous_work/helios_trace.csv filter=lfs diff=lfs merge=lfs -text
+data/job_trace/trace_previous_work/pai_trace.csv filter=lfs diff=lfs merge=lfs -text
+data/job_trace/trace_previous_work/philly_trace.csv filter=lfs diff=lfs merge=lfs -text
+data/job_trace/trace_previous_work/philly_trace_merge_retry.csv filter=lfs diff=lfs merge=lfs -text
+data/job_trace/trace_seren.csv filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,114 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+*.out
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/en/_build/
+docs/zh_cn/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# test
+test.json
+*.out
+*.inp
+
+# data/job_trace/trace_previous_work
+# data/job_trace/trace_seren.csv
+# data/utilization/ipmi
+# data/utilization/seren
+# data/utilization/kalos

LICENSE.txt

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README.md

@@ -0,0 +1,138 @@
+# Acme Trace
+
+This repository hosts the public releases of Acme traces from the Shanghai AI Lab, encompassing workloads spanning from March 2023 to August 2023. We encourage anyone to use the traces for academic purposes, and if you had any questions, feel free to send an email to us, or file an issue on Github. 
+
+Furthermore, we have conducted a thorough analysis of the Acme workloads, detailed in our NSDI '24 paper titled [Characterization of Large Language Model Development in the Datacenter](https://www.usenix.org/conference/nsdi24/presentation/hu). 
+
+<!-- ### **Note that due to space constraints on GitHub, our cluster utilization files are not hosted here. If you're interested in accessing these files, they are available on HuggingFace (~80GB).** 
+<span style="font-size:20px;font-weight:bold;"> Link:</span> [<img src="https://huggingface.co/datasets/huggingface/badges/raw/main/dataset-on-hf-sm.svg">](https://huggingface.co/datasets/Qinghao/AcmeTrace) -->
+
+<!-- **[Acme Full Dataset](https://huggingface.co/datasets/Qinghao/AcmeTrace)** -->
+
+# Acme Dataset
+
+The main trace characteristics, dataset structure and schema are:
+
+## Main Characteristics:
+*	Full Dataset size: 80GB (on HuggingFace)
+*   Dataset size: 109MB
+*	Duration: 6 months
+*   Number of independent GPU clusters: 2
+*	Total number of jobs: 880,740
+*	Total number of GPU jobs: 470,497
+
+## Dataset Structure
+
+```
+📦AcmeTrace
+ ┣ 📂data
+ ┃ ┣ 📂job_trace 
+ ┃ ┃ ┣ 📂trace_previous_work              (Prior job traces for comparison)
+ ┃ ┃ ┃ ┣ 📜helios_trace.csv
+ ┃ ┃ ┃ ┣ 📜xxx.csv
+ ┃ ┃ ┣ 📜trace_kalos.csv                  (Job trace file, collected from scheduler)
+ ┃ ┃ ┗ 📜trace_seren.csv
+ ┃ ┣ 📂utilization
+ ┃ ┃ ┣ 📂ipmi                             (Power of different server models in Seren, collected from IPMI)
+ ┃ ┃ ┃ ┣ 📜CPU_D_Power.csv
+ ┃ ┃ ┃ ┣ 📜GPU_AB_Power.csv
+ ┃ ┃ ┃ ┗ 📜GPU_C_Power.csv
+ ┃ ┃ ┣ 📂kalos                            (Resource utilization logs, collected from DCGM & Prometheus)
+ ┃ ┃ ┃ ┣ 📜DRAM_ACTIVE.csv
+ ┃ ┃ ┃ ┣ 📜xxx.csv
+ ┃ ┃ ┣ 📂seren
+ ┃ ┃ ┃ ┣ 📜DRAM_ACTIVE.csv
+ ┃ ┃ ┃ ┣ 📜xxx.csv
+ ┃ ┃ ┣ 📂util_pkl                         (Processed pickle files for plotting)
+ ┃ ┃ ┃ ┣ 📜gpu_power_kalos.pkl
+ ┃ ┃ ┃ ┣ 📜xxx.pkl
+ ┃ ┣ 📜cluster_summary.csv
+ ┃ ┣ 📜generate_utilization_pkl.ipynb     (Parse utilization files and generate pickles)
+ ┃ ┗ 📜utils.py
+ ┣ 📂figure                               (Examples of trace visualization)
+ ┃ ┣ 📜bar_job_state.pdf
+ ┃ ┣ 📜xxx.pdf
+ ┣ 📜LICENSE.txt
+ ┣ 📜README.md
+ ┗ 📜analysis.ipynb                       (Scripts for plotting)
+```
+
+## Schema and Description
+
+### 1. Job Trace
+
+#### Description
+
+Provides rich information on all jobs submitted to scheduler in each cluster.
+
++ `trace_seren.csv` Example
+
+| job_id  | user  | node_num | gpu_num | cpu_num | type  | state     | submit_time               | start_time                | end_time                  | duration | queue | gpu_time |
+|---------|-------|----------|---------|---------|-------|-----------|---------------------------|---------------------------|---------------------------|----------|-------|----------|
+| 5778432 | u5907 | 1        | 8       | 128     | Other | FAILED    | 2023-03-01 00:18:22+08:00 | 2023-03-01 00:18:54+08:00 | 2023-03-01 00:20:51+08:00 | 117      | 32    | 936.0    |
+| 5778469 | u5907 | 1        | 8       | 128     | Other | COMPLETED | 2023-03-01 00:23:58+08:00 | 2023-03-01 00:24:11+08:00 | 2023-03-01 01:09:04+08:00 | 2693     | 13    | 21544.0  |
+
+
++ `trace_kalos.csv` Example
+
+| job_id           | user  | node_num | gpu_num | cpu_num | mem_per_pod_GB | shared_mem_per_pod | type  | state     | submit_time               | start_time                | end_time                  | fail_time                 | stop_time                 | duration | queue | gpu_time |
+|------------------|-------|----------|---------|---------|----------------|--------------------|-------|-----------|---------------------------|---------------------------|---------------------------|---------------------------|---------------------------|----------|-------|----------|
+| dlctk696s0jbvitv | uf794 | 8        | 64      | 960     | 1000           | 100.0              | Other | FAILED    | 2023-05-17 11:00:58+00:00 | 2023-05-17 11:01:08+00:00 | 2023-05-17 11:01:16+00:00 | 2023-05-17 11:01:16+00:00 |                           | 18       | 10.0  | 1152.0   |
+| dlc1t2ypl09b8qtp | uf794 | 8        | 64      | 960     | 1000           | 100.0              | Other | CANCELLED | 2023-05-17 11:28:42+00:00 | 2023-05-17 11:28:54+00:00 | 2023-05-17 11:30:04+00:00 |                           | 2023-05-17 11:30:04+00:00 | 82       | 12.0  | 5248.0   |
+
+
+#### Schema
+
+| Field         | Description                                         |
+| ------------- | --------------------------------------------------- |
+| `job_id`      | unique id of the job                |
+| `user`        | hashed id for the user, prefix is '*u*'             |
+| `node_num`    | number of nodes in the job                          |
+| `gpu_num`     | number of GPUs required for the job                 |
+| `cpu_num`     | number of CPUs required for the job                 |
+| `type`     | workload type in LLM development                 |
+| `state`       | the job's status upon termination  <sup>1</sup>     |
+| `submit_time` | the job's submission time                           |
+| `start_time`  | the job's start execution time                      |
+| `end_time`    | the job's termination time                          |
+| `duration`    | total job execution time of the job <sup>2</sup>    |
+| `queue`       | total job queue time of the job <sup>3</sup>        |
+| `gpu_time`       | total GPU resource consumed by the job <sup>4</sup>        |
+
+Only in Kalos:
+| Field         | Description                                         |
+| ------------- | --------------------------------------------------- |
+| `mem_per_pod_GB`      | Pod memory resource configuration               |
+| `shared_mem_per_pod`        | Pod memory resource configuration             |
+| `fail_time`    | the time that failure occurs                          |
+| `stop_time`     | the time that job stops                 |
+
+
+
+#### Notes
+1. A job can end up with one of five statuses: (1) `COMPLETED`: it is finished successfully; (2) `CANCELLED`: it is terminated by the user; (3) `FAILED`: it is terminated due to internal or external errors; (4) `TIMEOUT`: the execution time is out of limit; (5) `NODE_FAIL`: it is terminated due to the node crash. `TIMEOUT` and `NODE_FAIL` are very rare in our traces, and are regarded as failed in our analysis.
+2. Calculated from the difference between `end_time` and `start_time`. (Unit: seconds)
+3. Calculated from the difference between `start_time` and `submit_time`. (Unit: seconds)
+4. Calculated from the product between `duration` and `gpu_num`.
+
+
+### 2. Resource Utilization
+
+
+#### Description
+
+Cluster resource utilization monitoring data, collected from DCGM, IPMI and Prometheus.
+
++ `NODE_CPU_UTILIZATION.csv` Example
+
+| Time                      | 10.140.1.10 | 10.140.1.54 | 10.140.1.90 | 10.140.1.41 | 10.140.1.98 | 10.140.0.166 | 10.140.1.4 | 10.140.1.40 | 10.140.1.134 | 10.140.0.147 | 10.140.1.119 | 10.140.0.184 | 10.140.0.151 | 10.140.0.254 | 10.140.1.83 | 10.140.0.246 | 10.140.1.78 | 10.140.1.103 | 10.140.1.155 | 10.140.1.87 | 10.140.1.106 | 10.140.1.140 | 10.140.1.150 | 10.140.1.107 | 10.140.1.172 | 10.140.1.95 | 10.140.0.146 | 10.140.1.125 | 10.140.1.50 | 10.140.1.112 | 10.140.0.159 | 10.140.0.144 | 10.140.0.215 | 10.140.1.36 | 10.140.1.143 | 10.140.1.147 | 10.140.1.14 | 10.140.1.85 | 10.140.1.56 | 10.140.0.243 | 10.140.0.242 | 10.140.1.63 | 10.140.0.132 | 10.140.0.255 | 10.140.1.59 | 10.140.1.130 | 10.140.0.218 | 10.140.0.220 | 10.140.1.27 | 10.140.1.67 | 10.140.1.136 | 10.140.1.84 | 10.140.0.190 | 10.140.1.121 | 10.140.1.146 | 10.140.1.38 | 10.140.0.232 | 10.140.1.18 | 10.140.1.66 | 10.140.0.205 | 10.140.1.154 | 10.140.1.170 | 10.140.0.179 | 10.140.0.135 | 10.140.1.102 | 10.140.1.72 | 10.140.0.249 | 10.140.1.138 | 10.140.1.24 | 10.140.1.60 | 10.140.1.82 | 10.140.0.233 | 10.140.1.23 | 10.140.0.241 | 10.140.0.248 | 10.140.1.68 | 10.140.1.1 | 10.140.0.219 | 10.140.1.116 | 10.140.0.157 | 10.140.0.178 | 10.140.1.29 | 10.140.1.57 | 10.140.0.163 | 10.140.1.52 | 10.140.1.177 | 10.140.1.11 | 10.140.1.26 | 10.140.1.34 | 10.140.1.92 | 10.140.0.211 | 10.140.0.161 | 10.140.0.131 | 10.140.1.124 | 10.140.0.238 | 10.140.1.44 | 10.140.0.237 | 10.140.1.79 | 10.140.1.17 | 10.140.0.214 | 10.140.1.153 | 10.140.1.117 | 10.140.1.109 | 10.140.0.167 | 10.140.0.207 | 10.140.0.134 | 10.140.1.99 | 10.140.1.31 | 10.140.1.127 | 10.140.0.250 | 10.140.1.139 | 10.140.1.53 | 10.140.1.123 | 10.140.1.77 | 10.140.0.133 | 10.140.0.251 | 10.140.1.55 | 10.140.1.12 | 10.140.1.19 | 10.140.1.47 | 10.140.1.118 | 10.140.1.61 | 10.140.1.110 | 10.140.1.64 | 10.140.1.129 | 10.140.0.217 | 10.140.1.104 | 10.140.0.244 | 10.140.0.213 | 10.140.1.97 | 10.140.0.136 | 10.140.1.22 | 10.140.1.32 | 10.140.1.171 | 10.140.1.151 | 10.140.1.96 | 10.140.1.46 | 10.140.0.158 | 10.140.1.51 | 10.140.1.86 | 10.140.1.30 | 10.140.0.156 | 10.140.1.43 | 10.140.1.74 | 10.140.1.89 | 10.140.1.169 | 10.140.1.80 | 10.140.1.2 | 10.140.1.108 | 10.140.1.93 | 10.140.1.73 | 10.140.0.180 | 10.140.1.71 | 10.140.1.88 | 10.140.0.209 | 10.140.1.81 | 10.140.0.152 | 10.140.1.28 | 10.140.1.58 | 10.140.0.236 | 10.140.0.138 | 10.140.0.149 | 10.140.0.206 | 10.140.1.15 | 10.140.0.240 | 10.140.0.203 | 10.140.1.5 | 10.140.1.37 | 10.140.0.143 | 10.140.0.160 | 10.140.0.252 | 10.140.1.75 | 10.140.1.115 | 10.140.0.247 | 10.140.1.6 | 10.140.1.16 | 10.140.0.216 | 10.140.0.150 | 10.140.1.25 | 10.140.0.208 | 10.140.1.62 | 10.140.1.173 | 10.140.1.137 | 10.140.1.9 | 10.140.1.65 | 10.140.1.111 | 10.140.1.135 | 10.140.1.114 | 10.140.1.132 | 10.140.0.154 | 10.140.0.204 | 10.140.1.91 | 10.140.1.120 | 10.140.1.105 | 10.140.1.131 | 10.140.0.165 | 10.140.0.210 | 10.140.0.148 | 10.140.1.133 | 10.140.0.239 | 10.140.1.13 | 10.140.1.144 | 10.140.0.137 | 10.140.0.234 | 10.140.1.142 | 10.140.1.168 | 10.140.0.235 | 10.140.0.140 | 10.140.1.39 | 10.140.0.153 | 10.140.0.139 | 10.140.1.3 | 10.140.1.7 | 10.140.1.94 | 10.140.1.145 | 10.140.1.149 | 10.140.1.152 | 10.140.1.35 | 10.140.0.141 | 10.140.1.69 | 10.140.1.100 | 10.140.1.126 | 10.140.0.142 | 10.140.0.185 | 10.140.1.42 | 10.140.0.231 | 10.140.0.253 | 10.140.0.212 | 10.140.1.21 | 10.140.1.148 | 10.140.1.49 | 10.140.1.128 | 10.140.0.164 | 10.140.1.70 | 10.140.1.45 | 10.140.0.162 | 10.140.1.101 | 10.140.0.145 | 10.140.1.20 | 10.140.1.176 | 10.140.1.33 | 10.140.1.113 | 10.140.1.122 | 10.140.1.76 | 10.140.1.141 | 10.140.1.8 | 10.140.0.155 | 10.140.1.48 |
+|---------------------------|-------------|-------------|-------------|-------------|-------------|--------------|------------|-------------|--------------|--------------|--------------|--------------|--------------|--------------|-------------|--------------|-------------|--------------|--------------|-------------|--------------|--------------|--------------|--------------|--------------|-------------|--------------|--------------|-------------|--------------|--------------|--------------|--------------|-------------|--------------|--------------|-------------|-------------|-------------|--------------|--------------|-------------|--------------|--------------|-------------|--------------|--------------|--------------|-------------|-------------|--------------|-------------|--------------|--------------|--------------|-------------|--------------|-------------|-------------|--------------|--------------|--------------|--------------|--------------|--------------|-------------|--------------|--------------|-------------|-------------|-------------|--------------|-------------|--------------|--------------|-------------|------------|--------------|--------------|--------------|--------------|-------------|-------------|--------------|-------------|--------------|-------------|-------------|-------------|-------------|--------------|--------------|--------------|--------------|--------------|-------------|--------------|-------------|-------------|--------------|--------------|--------------|--------------|--------------|--------------|--------------|-------------|-------------|--------------|--------------|--------------|-------------|--------------|-------------|--------------|--------------|-------------|-------------|-------------|-------------|--------------|-------------|--------------|-------------|--------------|--------------|--------------|--------------|--------------|-------------|--------------|-------------|-------------|--------------|--------------|-------------|-------------|--------------|-------------|-------------|-------------|--------------|-------------|-------------|-------------|--------------|-------------|------------|--------------|-------------|-------------|--------------|-------------|-------------|--------------|-------------|--------------|-------------|-------------|--------------|--------------|--------------|--------------|-------------|--------------|--------------|------------|-------------|--------------|--------------|--------------|-------------|--------------|--------------|------------|-------------|--------------|--------------|-------------|--------------|-------------|--------------|--------------|------------|-------------|--------------|--------------|--------------|--------------|--------------|--------------|-------------|--------------|--------------|--------------|--------------|--------------|--------------|--------------|--------------|-------------|--------------|--------------|--------------|--------------|--------------|--------------|--------------|-------------|--------------|--------------|------------|------------|-------------|--------------|--------------|--------------|-------------|--------------|-------------|--------------|--------------|--------------|--------------|-------------|--------------|--------------|--------------|-------------|--------------|-------------|--------------|--------------|-------------|-------------|--------------|--------------|--------------|-------------|--------------|-------------|--------------|--------------|-------------|--------------|------------|--------------|-------------|
+| 2023-07-01 08:00:00+08:00 | 8.101       | 7.809       | 8.034       | 0.437       | 0.672       | 8.988        | 8.395      | 8.205       | 8.763        | 2.037        | 6.661        | 9.177        | 9.017        | 8.096        | 14.423      | 8.04         | 0.354       | 0.34         | 0.843        | 8.66        | 0.657        | 8.104        | 0.902        | 7.006        | 0.107        | 8.298       | 8.546        | 6.413        | 8.1         | 6.633        | 8.167        | 9.246        | 9.055        | 2.963       | 7.995        | 0.707        | 8.119       | 10.531      | 6.654       | 7.707        | 4.626        | 0.848       | 25.274       | 7.95         | 8.014       | 7.908        | 9.313        | 9.184        | 7.877       | 0.484       | 8.451        | 6.137       | 0.124        | 6.163        | 0.316        | 8.343       | 9.024        | 7.922       | 8.427       | 0.455        | 67.47        | 0.395        | 7.487        | 9.142        | 7.898        | 8.071       | 7.717        | 0.755        | 7.869       | 8.193       | 8.368       | 8.911        | 8.108       | 7.934        | 8.269        | 8.161       | 8.349      | 9.252        | 6.933        | 4.823        | 7.527        | 8.42        | 7.243       | 9.166        | 8.04        | 0.092        | 7.921       | 8.28        | 8.027       | 0.365       | 8.71         | 9.302        | 0.88         | 8.055        | 8.817        | 8.07        | 9.316        | 8.064       | 8.061       | 9.319        | 7.101        | 5.221        | 7.086        | 7.701        | 9.259        | 8.857        | 5.079       | 7.944       | 8.02         | 8.244        | 8.038        | 8.269       | 5.108        | 6.971       | 1.787        | 8.095        | 8.055       | 8.275       | 8.396       | 7.787       | 6.898        | 8.224       | 16.323       | 0.671       | 8.071        | 9.125        | 8.004        | 7.888        | 8.785        | 5.412       | 0.621        | 8.004       | 7.91        | 6.727        | 10.327       | 0.413       | 8.499       | 7.735        | 8.255       | 8.087       | 8.001       | 5.908        | 8.239       | 8.279       | 7.272       | 0.14         | 8.186       | 0.526      | 6.771        | 6.386       | 6.763       | 7.308        | 6.741       | 8.047       | 8.883        | 7.059       | 8.79         | 7.864       | 8.065       | 9.474        | 0.481        | 9.179        | 9.579        | 8.157       | 9.063        | 7.339        | 8.295      | 6.81        | 9.029        | 9.037        | 8.042        | 0.717       | 6.675        | 7.838        | 8.192      | 8.038       | 9.004        | 8.621        | 8.117       | 8.177        | 22.467      | 0.198        | 3.4          | 8.086      | 7.86        | 6.891        | 4.376        | 7.144        | 5.331        | 8.924        | 7.668        | 0.332       | 7.961        | 7.958        | 8.164        | 5.741        | 8.938        | 8.969        | 6.372        | 8.816        | 8.361       | 12.62        | 9.149        | 9.151        | 8.374        | 8.831        | 9.332        | 9.181        | 8.142       | 8.653        | 1.449        | 8.268      | 8.481      | 8.568       | 0.468        | 59.942       | 66.076       | 8.191       | 8.96         | 8.223       | 0.478        | 8.023        | 9.129        | 9.6          | 8.164       | 9.518        | 8.172        | 9.551        | 8.012       | 14.544       | 8.154       | 8.069        | 9.344        | 0.357       | 8.09        | 0.463        | 8.082        | 7.657        | 8.139       | 0.164        | 8.143       | 6.56         | 6.632        | 8.018       | 8.065        | 8.288      | 8.667        | 8.078       |
+
+
+#### Schema
+
+| Field   | Description                             |
+| ------- | --------------------------------------- |
+| `Time`  | sampling timestamp, interval is 15 seconds             |
+| `10.140.xx.xx` | server ip            |

analysis.ipynb

@@ -0,0 +1,1111 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "import os\n",
+    "import pickle\n",
+    "import squarify\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patches as mpatches\n",
+    "from matplotlib.lines import Line2D\n",
+    "\n",
+    "SAVEPATH = \"./figure\"\n",
+    "TRACEPATH = \"./data/job_trace\"\n",
+    "PKLPATH = \"./data/utilization/util_pkl\"\n",
+    "\n",
+    "sns.set_style(\"ticks\")\n",
+    "font = {\n",
+    "    \"font.family\": \"Roboto\",\n",
+    "    \"font.size\": 12,\n",
+    "}\n",
+    "sns.set_style(font)\n",
+    "paper_rc = {\n",
+    "    \"lines.linewidth\": 3,\n",
+    "    \"lines.markersize\": 10,\n",
+    "}\n",
+    "sns.set_context(\"paper\", font_scale=2, rc=paper_rc)\n",
+    "cmp = sns.color_palette(\"tab10\")\n",
+    "\n",
+    "\n",
+    "def autolabel(rects, ax, prec=1):\n",
+    "    \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n",
+    "    for rect in rects:\n",
+    "        height = rect.get_height()\n",
+    "        ax.annotate(\n",
+    "            f\"{height:.{prec}f}\",\n",
+    "            xy=(rect.get_x() + rect.get_width() / 2, height),\n",
+    "            xytext=(0, 3),  # 3 points vertical offset\n",
+    "            textcoords=\"offset points\",\n",
+    "            ha=\"center\",\n",
+    "            va=\"bottom\",\n",
+    "            size=16,\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "def calculate_num_cdf_customized_xaxis(df: pd.DataFrame, x_axis: List, key: str):\n",
+    "    \"\"\"\n",
+    "    Calculate quantity percentile CDF with customized threshold of x-axis, y-axis: 0-100%,\n",
+    "    \"\"\"\n",
+    "    # print(\"Parsing\")\n",
+    "    data = df[[key]].copy()\n",
+    "    data.dropna(inplace=True)\n",
+    "\n",
+    "    y = [len(data[data[key] <= x]) / len(data) * 100 for x in x_axis]\n",
+    "\n",
+    "    return y\n",
+    "\n",
+    "\n",
+    "def calculate_sum_cdf_customized_xaxis(df: pd.DataFrame, x_axis: List, key: str, key_to_time=None):\n",
+    "    \"\"\"\n",
+    "    Calculate sum CDF with customized threshold of x-axis, y-axis: 0-100%,\n",
+    "    \"\"\"\n",
+    "    if key_to_time is not None:\n",
+    "        data = df[[key, key_to_time]].copy()\n",
+    "        data[\"new\"] = data[key] * data[key_to_time]\n",
+    "    else:\n",
+    "        data = df[[key]].copy()\n",
+    "        data[\"new\"] = data[key]\n",
+    "    data.dropna(inplace=True)\n",
+    "    sum = data[\"new\"].sum()\n",
+    "\n",
+    "    y = [data[data[key] <= x][\"new\"].sum() / sum * 100 for x in x_axis]\n",
+    "\n",
+    "    return y\n",
+    "\n",
+    "\n",
+    "if not os.path.exists(SAVEPATH):\n",
+    "    os.makedirs(SAVEPATH)\n",
+    "\n",
+    "\n",
+    "data_seren = pd.read_csv(f\"{TRACEPATH}/trace_seren.csv\")\n",
+    "data_kalos = pd.read_csv(f\"{TRACEPATH}/trace_kalos.csv\")\n",
+    "data_philly = pd.read_csv(f\"{TRACEPATH}/trace_previous_work/philly_trace.csv\")\n",
+    "data_helios = pd.read_csv(f\"{TRACEPATH}/trace_previous_work/helios_trace.csv\")\n",
+    "data_pai = pd.read_csv(f\"{TRACEPATH}/trace_previous_work/pai_trace.csv\")\n",
+    "\n",
+    "# A few further process\n",
+    "data_pai.rename(columns={\"plan_cpu\": \"cpu_num\", \"plan_gpu\": \"gpu_num\", \"wait_time\": \"queue\", \"status\": \"state\"}, inplace=True)\n",
+    "data_pai[[\"cpu_num\", \"gpu_num\"]] /= 100\n",
+    "data_pai[\"state\"] = data_pai[\"state\"].map({\"Failed\": \"FAILED\"})  # Not suitable for final state analysis\n",
+    "data_philly[\"state\"] = data_philly[\"state\"].map({\"Pass\": \"COMPLETED\", \"Failed\": \"FAILED\", \"Killed\": \"CANCELLED\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CDF: GPU Job Duration & Utilization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = [2**i for i in range(0, 22)]\n",
+    "y_gpu_seren = calculate_num_cdf_customized_xaxis(data_seren[data_seren[\"gpu_num\"] > 0], x_axis=x, key=\"duration\")\n",
+    "y_gpu_kalos = calculate_num_cdf_customized_xaxis(data_kalos[data_kalos[\"gpu_num\"] > 0], x_axis=x, key=\"duration\")\n",
+    "y_gpu_philly = calculate_num_cdf_customized_xaxis(data_philly[data_philly[\"gpu_num\"] > 0], x_axis=x, key=\"duration\")\n",
+    "y_gpu_helios = calculate_num_cdf_customized_xaxis(data_helios[data_helios[\"gpu_num\"] > 0], x_axis=x, key=\"duration\")\n",
+    "y_gpu_pai = calculate_num_cdf_customized_xaxis(data_pai[data_pai[\"gpu_num\"] > 0], x_axis=x, key=\"duration\")\n",
+    "\n",
+    "with open(f\"{PKLPATH}/util_gpu_seren.pkl\", \"rb\") as file:\n",
+    "    x1, y1, _, _, _, _, _, _, _, _ = pickle.load(file)\n",
+    "\n",
+    "with open(f\"{PKLPATH}/util_gpu_kalos.pkl\", \"rb\") as file:\n",
+    "    x4, y4, _, _ = pickle.load(file)\n",
+    "\n",
+    "with open(f\"{PKLPATH}/util_gpu_pai.pkl\", \"rb\") as file:  # Collect via Antman\n",
+    "    x2, y2 = pickle.load(file)\n",
+    "\n",
+    "with open(f\"{PKLPATH}/util_gpu_philly.pkl\", \"rb\") as file:\n",
+    "    x3, y3 = pickle.load(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "linestyles = [\"-\", \"--\", \":\", \":\", \":\"]\n",
+    "grid_params = dict(width_ratios=[1, 1])\n",
+    "fig, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, constrained_layout=True, figsize=(9, 3.75))\n",
+    "\n",
+    "ax1.plot(x, y_gpu_seren, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Seren\")\n",
+    "ax1.plot(x, y_gpu_kalos, linestyles[1], linewidth=3, alpha=0.9, color=cmp[1], label=\"Kalos\")\n",
+    "ax1.plot(x, y_gpu_philly, linestyles[2], linewidth=3, alpha=0.9, color=cmp[2], label=\"Philly\")\n",
+    "ax1.plot(x, y_gpu_helios, linestyles[3], linewidth=3, alpha=0.9, color=cmp[3], label=\"Helios\")\n",
+    "ax1.plot(x, y_gpu_pai, linestyles[3], linewidth=3, alpha=0.9, color=cmp[4], label=\"PAI\")\n",
+    "\n",
+    "ax2.plot(x1, y1, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Seren\")\n",
+    "ax2.plot(x4, y4, linestyles[1], linewidth=3, alpha=0.9, color=cmp[1], label=\"Kalos\")\n",
+    "ax2.plot(x2, y2, linestyles[2], linewidth=3, alpha=0.9, color=cmp[4], label=\"PAI\")\n",
+    "ax2.plot(x3, y3, linestyles[2], linewidth=3, alpha=0.9, color=cmp[2], label=\"Philly\")\n",
+    "\n",
+    "ax1.set_xlabel(f\"(a) GPU Job Duration (s)\")\n",
+    "ax1.set_ylabel(f\"CDF (%)\")\n",
+    "ax1.set_xscale(\"log\")\n",
+    "ax1.set_xticks([1e0, 1e1, 1e2, 1e3, 1e4, 1e5, 1e6])\n",
+    "ax1.set_xlim(1, x[-1])\n",
+    "ax1.set_ylim(-0.5, 100.8)\n",
+    "ax1.grid(linestyle=\":\")\n",
+    "\n",
+    "ax2.set_xlabel(f\"(b) GPU Utilization (%)\")\n",
+    "ax2.set_ylabel(f\"CDF (%)\")\n",
+    "ax2.set_xlim(-0.8, 100.8)\n",
+    "ax2.set_xticks([0, 25, 50, 75, 100])\n",
+    "ax2.set_ylim(0, 100.8)\n",
+    "ax2.grid(linestyle=\":\")\n",
+    "\n",
+    "handles, labels = ax1.get_legend_handles_labels()\n",
+    "fig.legend(handles=handles, labels=labels, ncols=5, bbox_to_anchor=(0.1, 1.145), loc=2, columnspacing=1.5, handletextpad=0.5)\n",
+    "\n",
+    "sns.despine()\n",
+    "fig.savefig(f\"{SAVEPATH}/cdf_job_duration_util.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CDF: GPU Number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = [i for i in range(0, 1025)]\n",
+    "y_gpu_seren = calculate_num_cdf_customized_xaxis(data_seren[data_seren[\"gpu_num\"] > 0], x_axis=x, key=\"gpu_num\")\n",
+    "y_gpu_kalos = calculate_num_cdf_customized_xaxis(data_kalos[data_kalos[\"gpu_num\"] > 0], x_axis=x, key=\"gpu_num\")\n",
+    "y_gpu_philly = calculate_num_cdf_customized_xaxis(data_philly[data_philly[\"gpu_num\"] > 0], x_axis=x, key=\"gpu_num\")\n",
+    "y_gpu_helios = calculate_num_cdf_customized_xaxis(data_helios[data_helios[\"gpu_num\"] > 0], x_axis=x, key=\"gpu_num\")\n",
+    "y_gpu_pai = calculate_num_cdf_customized_xaxis(data_pai[data_pai[\"gpu_num\"] > 0], x_axis=x, key=\"gpu_num\")\n",
+    "\n",
+    "y_gtime_seren = calculate_sum_cdf_customized_xaxis(\n",
+    "    data_seren[data_seren[\"gpu_num\"] > 0], x_axis=x, key=\"gpu_num\", key_to_time=\"duration\"\n",
+    ")\n",
+    "y_gtime_kalos = calculate_sum_cdf_customized_xaxis(\n",
+    "    data_kalos[data_kalos[\"gpu_num\"] > 0], x_axis=x, key=\"gpu_num\", key_to_time=\"duration\"\n",
+    ")\n",
+    "y_gtime_philly = calculate_sum_cdf_customized_xaxis(\n",
+    "    data_philly[data_philly[\"gpu_num\"] > 0], x_axis=x, key=\"gpu_num\", key_to_time=\"duration\"\n",
+    ")\n",
+    "y_gtime_helios = calculate_sum_cdf_customized_xaxis(\n",
+    "    data_helios[data_helios[\"gpu_num\"] > 0], x_axis=x, key=\"gpu_num\", key_to_time=\"duration\"\n",
+    ")\n",
+    "y_gtime_pai = calculate_sum_cdf_customized_xaxis(\n",
+    "    data_pai[data_pai[\"gpu_num\"] > 0], x_axis=x, key=\"gpu_num\", key_to_time=\"duration\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "linestyles = [\"-\", \"--\", \":\", \":\", \":\"]\n",
+    "grid_params = dict(width_ratios=[1, 1])\n",
+    "fig, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, constrained_layout=True, figsize=(9, 3.75))\n",
+    "\n",
+    "ax1.plot(x, y_gpu_seren, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Seren\")\n",
+    "ax1.plot(x, y_gpu_kalos, linestyles[1], linewidth=3, alpha=0.9, color=cmp[1], label=\"Kalos\")\n",
+    "ax1.plot(x, y_gpu_philly, linestyles[2], linewidth=3, alpha=0.9, color=cmp[2], label=\"Philly\")\n",
+    "ax1.plot(x, y_gpu_helios, linestyles[3], linewidth=3, alpha=0.9, color=cmp[3], label=\"Helios\")\n",
+    "ax1.plot(x, y_gpu_pai, linestyles[3], linewidth=3, alpha=0.9, color=cmp[4], label=\"PAI\")\n",
+    "\n",
+    "ax2.plot(x, y_gtime_seren, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Seren\")\n",
+    "ax2.plot(x, y_gtime_kalos, linestyles[1], linewidth=3, alpha=0.9, color=cmp[1], label=\"Kalos\")\n",
+    "ax2.plot(x, y_gtime_philly, linestyles[2], linewidth=3, alpha=0.9, color=cmp[2], label=\"Philly\")\n",
+    "ax2.plot(x, y_gtime_helios, linestyles[3], linewidth=3, alpha=0.9, color=cmp[3], label=\"Helios\")\n",
+    "ax2.plot(x, y_gtime_pai, linestyles[3], linewidth=3, alpha=0.9, color=cmp[4], label=\"PAI\")\n",
+    "\n",
+    "\n",
+    "ax1.set_xlabel(f\"(a) Number of GPU\")\n",
+    "ax1.set_ylabel(f\"CDF of Jobs (%)\")\n",
+    "ax1.set_xscale(\"log\", base=2)\n",
+    "ax1.set_xticks([2**i for i in range(0, 11, 2)])\n",
+    "ax1.set_xticklabels(\n",
+    "    [2**i for i in range(0, 10, 2)]\n",
+    "    + [\n",
+    "        \"1024+\",\n",
+    "    ]\n",
+    ")\n",
+    "ax1.set_xlim(1, x[-1] + 1)\n",
+    "ax1.set_ylim(-0.5, 100.8)\n",
+    "ax1.grid(linestyle=\":\")\n",
+    "\n",
+    "ax2.set_xlabel(f\"(b) Number of GPU\")\n",
+    "ax2.set_ylabel(f\"CDF of GPU Time (%)\")\n",
+    "ax2.set_xscale(\"log\", base=2)\n",
+    "ax2.set_xticks([2**i for i in range(0, 11, 2)])\n",
+    "ax2.set_xticklabels(\n",
+    "    [2**i for i in range(0, 10, 2)]\n",
+    "    + [\n",
+    "        \"1024+\",\n",
+    "    ]\n",
+    ")\n",
+    "ax2.set_xlim(1, x[-1] + 50)\n",
+    "ax2.set_ylim(-0.5, 100.8)\n",
+    "ax2.grid(linestyle=\":\")\n",
+    "\n",
+    "handles, labels = ax1.get_legend_handles_labels()\n",
+    "fig.legend(handles=handles, labels=labels, ncols=5, bbox_to_anchor=(0.1, 1.145), loc=2, columnspacing=1.5, handletextpad=0.5)\n",
+    "\n",
+    "sns.despine()\n",
+    "fig.savefig(f\"{SAVEPATH}/cdf_job_gpunum.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Bar: Job Final State"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"./data/cluster_summary.csv\", index_col=\"id\")\n",
+    "grid_params = dict(width_ratios=[1, 1])\n",
+    "fig, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, constrained_layout=True, figsize=(9, 3.75))\n",
+    "\n",
+    "x = np.arange(1, 3)\n",
+    "width = 0.22\n",
+    "p1 = ax1.bar(\n",
+    "    x - width,\n",
+    "    df.loc[[\"Seren\", \"Kalos\"], \"complete_rate_gpu\"] * 100,\n",
+    "    width,\n",
+    "    label=\"Completed\",\n",
+    "    alpha=0.8,\n",
+    "    linewidth=1,\n",
+    "    edgecolor=\"k\",\n",
+    ")\n",
+    "p2 = ax1.bar(\n",
+    "    x, df.loc[[\"Seren\", \"Kalos\"], \"cancel_rate_gpu\"] * 100, width, label=\"Canceled\", alpha=0.8, linewidth=1, edgecolor=\"k\"\n",
+    ")\n",
+    "p3 = ax1.bar(\n",
+    "    x + width, df.loc[[\"Seren\", \"Kalos\"], \"fail_rate_gpu\"] * 100, width, label=\"Failed\", alpha=0.8, linewidth=1, edgecolor=\"k\"\n",
+    ")\n",
+    "\n",
+    "p4 = ax2.bar(\n",
+    "    x - width,\n",
+    "    df.loc[[\"Seren\", \"Kalos\"], \"complete_rate_gpu_time\"] * 100,\n",
+    "    width,\n",
+    "    label=\"Completed\",\n",
+    "    alpha=0.8,\n",
+    "    linewidth=1,\n",
+    "    edgecolor=\"k\",\n",
+    ")\n",
+    "p5 = ax2.bar(\n",
+    "    x, df.loc[[\"Seren\", \"Kalos\"], \"cancel_rate_gpu_time\"] * 100, width, label=\"Canceled\", alpha=0.8, linewidth=1, edgecolor=\"k\"\n",
+    ")\n",
+    "p6 = ax2.bar(\n",
+    "    x + width,\n",
+    "    df.loc[[\"Seren\", \"Kalos\"], \"fail_rate_gpu_time\"] * 100,\n",
+    "    width,\n",
+    "    label=\"Failed\",\n",
+    "    alpha=0.8,\n",
+    "    linewidth=1,\n",
+    "    edgecolor=\"k\",\n",
+    ")\n",
+    "\n",
+    "autolabel(p1, ax1)\n",
+    "autolabel(p2, ax1)\n",
+    "autolabel(p3, ax1)\n",
+    "autolabel(p4, ax2)\n",
+    "autolabel(p5, ax2)\n",
+    "autolabel(p6, ax2)\n",
+    "\n",
+    "ax1.set_xlabel(f\"(a) Job Count\")\n",
+    "ax1.set_ylabel(f\"Fraction (%)\")\n",
+    "ax1.set_xticks(x)\n",
+    "ax1.set_xticklabels([\"Seren\", \"Kalos\"])\n",
+    "ax1.set_xlim(0.5, 2.5)\n",
+    "ax1.set_ylim(0, 100)\n",
+    "ax1.grid(axis=\"y\", linestyle=\":\")\n",
+    "\n",
+    "ax2.set_xlabel(f\"(b) GPU Time\")\n",
+    "ax2.set_ylabel(f\"Fraction (%)\")\n",
+    "ax2.set_xticks(x)\n",
+    "ax2.set_xticklabels([\"Seren\", \"Kalos\"])\n",
+    "ax2.set_xlim(0.5, 2.5)\n",
+    "ax2.set_ylim(0, 100)\n",
+    "ax2.grid(axis=\"y\", linestyle=\":\")\n",
+    "\n",
+    "handles, labels = ax1.get_legend_handles_labels()\n",
+    "fig.legend(handles=handles, labels=labels, ncols=5, bbox_to_anchor=(0.18, 1.145), loc=2)\n",
+    "\n",
+    "sns.despine()\n",
+    "fig.savefig(f\"{SAVEPATH}/bar_job_state.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Treemap: Job Number Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Processing Seren\")\n",
+    "datas = data_seren[data_seren[\"gpu_num\"] > 0]\n",
+    "\n",
+    "job_type = [\"Eval\", \"Pretrain\", \"SFT\", \"MLLM\", \"Debug\", \"Other\"]\n",
+    "df = pd.DataFrame(index=job_type, columns=[\"job_count\"]).fillna(0)\n",
+    "df[\"job_count\"] = df.index.map(datas.groupby(\"type\").size()).astype(int)\n",
+    "df[\"gtime\"] = df.index.map(datas.groupby(\"type\")[\"gpu_time\"].sum()).astype(int)\n",
+    "\n",
+    "total = df[\"job_count\"].sum()\n",
+    "total_gtime = df[\"gtime\"].sum()\n",
+    "\n",
+    "df[\"count_percent\"] = df[\"job_count\"] / total * 100\n",
+    "df[\"gtime_percent\"] = df[\"gtime\"] / total_gtime * 100\n",
+    "\n",
+    "# For plotting\n",
+    "df[\"label\"] = [x + f\"\\n{df.at[x, 'count_percent']:.1f}%\" for x in list(df.index)]\n",
+    "df[\"label_gtime\"] = [x + f\"\\n{df.at[x, 'gtime_percent']:.1f}%\" for x in list(df.index)]\n",
+    "df[\"label_percent\"] = [f\"{df.at[x, 'count_percent']:.1f}%\" for x in list(df.index)]\n",
+    "df[\"label_gtime_percent\"] = [f\"{df.at[x, 'gtime_percent']:.1f}%\" for x in list(df.index)]\n",
+    "df_s = df.copy()\n",
+    "\n",
+    "print(\"Processing Kalos\")\n",
+    "datak = data_kalos[data_kalos[\"gpu_num\"] > 0]\n",
+    "\n",
+    "job_type = [\"Eval\", \"Pretrain\", \"Debug\", \"Other\"]\n",
+    "df = pd.DataFrame(index=job_type, columns=[\"job_count\"]).fillna(0)\n",
+    "df[\"job_count\"] = df.index.map(datak.groupby(\"type\").size()).astype(int)\n",
+    "df[\"gtime\"] = df.index.map(datak.groupby(\"type\")[\"gpu_time\"].sum()).astype(int)\n",
+    "\n",
+    "\n",
+    "total = df[\"job_count\"].sum()\n",
+    "total_gtime = df[\"gtime\"].sum()\n",
+    "\n",
+    "df[\"count_percent\"] = df[\"job_count\"] / total * 100\n",
+    "df[\"gtime_percent\"] = df[\"gtime\"] / total_gtime * 100\n",
+    "\n",
+    "# For plotting\n",
+    "df[\"label\"] = [x + f\"\\n{df.at[x, 'count_percent']:.1f}%\" for x in list(df.index)]\n",
+    "df[\"label_gtime\"] = [x + f\"\\n{df.at[x, 'gtime_percent']:.1f}%\" for x in list(df.index)]\n",
+    "df[\"label_percent\"] = [f\"{df.at[x, 'count_percent']:.1f}\\n%\" for x in list(df.index)]\n",
+    "df[\"label_gtime_percent\"] = [f\"{df.at[x, 'gtime_percent']:.1f}\\n%\" for x in list(df.index)]\n",
+    "df_k = df.copy()\n",
+    "\n",
+    "# For plotting\n",
+    "df_k.at[\"Pretrain\", \"label_gtime_percent\"] = df_k.at[\"Pretrain\", \"label_gtime\"]\n",
+    "df_k.at[\"Eval\", \"label_percent\"] = df_k.at[\"Eval\", \"label\"]\n",
+    "df_k.at[\"Other\", \"label_percent\"] = \"\"\n",
+    "df_k.at[\"Eval\", \"label_gtime_percent\"] = \" \"\n",
+    "\n",
+    "df_s.at[\"Pretrain\", \"label_gtime_percent\"] = df_s.at[\"Pretrain\", \"label_gtime\"]\n",
+    "df_s.at[\"Eval\", \"label_percent\"] = df_s.at[\"Eval\", \"label\"]\n",
+    "df_s.at[\"SFT\", \"label_percent\"] = df_s.at[\"SFT\", \"label\"]\n",
+    "df_s.at[\"Other\", \"label_percent\"] = df_s.at[\"Other\", \"label\"]\n",
+    "df_s.at[\"Pretrain\", \"label_percent\"] = \" \"\n",
+    "df_s.at[\"Debug\", \"label_gtime_percent\"] = df_s.at[\"Debug\", \"label_gtime_percent\"].replace(\"%\", \"\\n%\")\n",
+    "\n",
+    "\n",
+    "cmp_treemap = sns.color_palette(\"pastel\")\n",
+    "label = df_s.index.to_list()\n",
+    "df_s[\"color\"] = cmp_treemap[: len(df_s)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(ncols=2, nrows=2, constrained_layout=True, figsize=(9, 4.2))\n",
+    "FONT = 15\n",
+    "\n",
+    "###### Fig 1 ######\n",
+    "df_s.sort_values(by=\"count_percent\", ascending=False, inplace=True)\n",
+    "squarify.plot(\n",
+    "    ax=ax1,\n",
+    "    sizes=list(df_s[\"job_count\"].values),\n",
+    "    label=df_s[\"label_percent\"],\n",
+    "    text_kwargs={\"fontsize\": FONT},\n",
+    "    color=df_s[\"color\"],\n",
+    "    bar_kwargs={\"alpha\": 0.8, \"linewidth\": 1, \"edgecolor\": \"k\"},\n",
+    ")\n",
+    "\n",
+    "\n",
+    "handles, labels = ax1.get_legend_handles_labels()\n",
+    "handles_new = [handles[0], handles[-1], handles[1], handles[3], handles[4], handles[2]]\n",
+    "fig.legend(\n",
+    "    handles=handles_new, labels=label, ncols=6, bbox_to_anchor=(0.0, 1.135), loc=2, columnspacing=0.82, handletextpad=0.2\n",
+    ")\n",
+    "\n",
+    "###### Fig 2 ######\n",
+    "df_s.sort_values(by=\"gtime\", ascending=False, inplace=True)\n",
+    "squarify.plot(\n",
+    "    ax=ax2,\n",
+    "    sizes=list(df_s[\"gtime\"].values),\n",
+    "    label=df_s[\"label_gtime_percent\"],\n",
+    "    text_kwargs={\"fontsize\": FONT},\n",
+    "    color=df_s[\"color\"],\n",
+    "    bar_kwargs={\"alpha\": 0.8, \"linewidth\": 1, \"edgecolor\": \"k\"},\n",
+    ")\n",
+    "\n",
+    "plt.tick_params(axis=\"both\", which=\"both\", bottom=False, top=False, left=False, right=False)\n",
+    "\n",
+    "ax1.set_xlabel(f\"(a) Job Count\", fontsize=16)\n",
+    "ax2.set_xlabel(f\"(b) GPU Time\", fontsize=16)\n",
+    "\n",
+    "\n",
+    "###### Fig 3 ######\n",
+    "df_k.sort_values(by=\"count_percent\", ascending=False, inplace=True)\n",
+    "df_k[\"color\"] = [df_s[\"color\"][job_name] for job_name in df_k.index]\n",
+    "\n",
+    "squarify.plot(\n",
+    "    ax=ax3,\n",
+    "    sizes=list(df_k[\"job_count\"].values),\n",
+    "    label=df_k[\"label_percent\"],\n",
+    "    text_kwargs={\"fontsize\": FONT},\n",
+    "    color=df_k[\"color\"],\n",
+    "    bar_kwargs={\"alpha\": 0.8, \"linewidth\": 1, \"edgecolor\": \"k\"},\n",
+    ")\n",
+    "\n",
+    "###### Fig 4 ######\n",
+    "df_k.sort_values(by=\"gtime\", ascending=False, inplace=True)\n",
+    "squarify.plot(\n",
+    "    ax=ax4,\n",
+    "    sizes=list(df_k[\"gtime\"].values),\n",
+    "    label=df_k[\"label_gtime_percent\"],\n",
+    "    text_kwargs={\"fontsize\": FONT},\n",
+    "    color=df_k[\"color\"],\n",
+    "    bar_kwargs={\"alpha\": 0.8, \"linewidth\": 1, \"edgecolor\": \"k\"},\n",
+    ")\n",
+    "\n",
+    "ax1.annotate(\n",
+    "    df_s.at[\"Pretrain\", \"label\"].split(\"\\n\")[1],\n",
+    "    xy=(97, 96),\n",
+    "    xytext=(90, 70),\n",
+    "    arrowprops=dict(facecolor=\"black\", width=2.5, headwidth=8),\n",
+    "    color=\"black\",\n",
+    "    fontsize=15,\n",
+    ")\n",
+    "\n",
+    "ax3.annotate(\n",
+    "    df_k.at[\"Other\", \"label\"].split(\"\\n\")[1],\n",
+    "    xy=(98.5, 92),\n",
+    "    xytext=(80, 80),\n",
+    "    arrowprops=dict(facecolor=\"black\", width=2.5, headwidth=8),\n",
+    "    color=\"black\",\n",
+    "    fontsize=15,\n",
+    ")\n",
+    "\n",
+    "ax4.annotate(\n",
+    "    df_k.at[\"Eval\", \"label_gtime\"].split(\"\\n\")[1],\n",
+    "    xy=(98.5, 93),\n",
+    "    xytext=(80, 80),\n",
+    "    arrowprops=dict(facecolor=\"black\", width=2.5, headwidth=8),\n",
+    "    color=\"black\",\n",
+    "    fontsize=15,\n",
+    ")\n",
+    "\n",
+    "plt.tick_params(axis=\"both\", which=\"both\", bottom=False, top=False, left=False, right=False)\n",
+    "\n",
+    "ax3.set_xlabel(f\"(c) Job Count\", fontsize=16)\n",
+    "ax4.set_xlabel(f\"(d) GPU Time\", fontsize=16, labelpad=8)\n",
+    "\n",
+    "ax1.set_xticks([])\n",
+    "ax1.set_yticks([])\n",
+    "ax2.set_xticks([])\n",
+    "ax2.set_yticks([])\n",
+    "ax3.set_xticks([])\n",
+    "ax3.set_yticks([])\n",
+    "ax4.set_xticks([])\n",
+    "ax4.set_yticks([])\n",
+    "\n",
+    "ax1.text(0.015, 0.03, \"Seren\", transform=ax1.transAxes, size=18, fontweight=\"bold\")\n",
+    "ax2.text(0.02, 0.03, \"Seren\", transform=ax2.transAxes, size=18, fontweight=\"bold\")\n",
+    "ax3.text(0.02, 0.03, \"Kalos\", transform=ax3.transAxes, size=18, fontweight=\"bold\")\n",
+    "ax4.text(0.02, 0.03, \"Kalos\", transform=ax4.transAxes, size=18, fontweight=\"bold\")\n",
+    "fig.savefig(f\"{SAVEPATH}/treemap_job_dist.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CDF: Duration and Queuing Delay of Different Type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "(a) Seren Duration (b) Seren Queuing  (c) Kalos Duration (d) Kalos Queuing\n",
+    "\"\"\"\n",
+    "\n",
+    "# Duration part\n",
+    "x = [2**i for i in range(0, 22)]\n",
+    "y_gpu_seren_other = calculate_num_cdf_customized_xaxis(\n",
+    "    data_seren[(data_seren[\"gpu_num\"] > 0) & (data_seren[\"type\"] == \"Other\")], x_axis=x, key=\"duration\"\n",
+    ")\n",
+    "y_gpu_seren_debug = calculate_num_cdf_customized_xaxis(\n",
+    "    data_seren[(data_seren[\"gpu_num\"] > 0) & (data_seren[\"type\"] == \"Debug\")], x_axis=x, key=\"duration\"\n",
+    ")\n",
+    "y_gpu_seren_pretrain = calculate_num_cdf_customized_xaxis(\n",
+    "    data_seren[(data_seren[\"gpu_num\"] > 0) & (data_seren[\"type\"] == \"Pretrain\")], x_axis=x, key=\"duration\"\n",
+    ")\n",
+    "y_gpu_seren_eval = calculate_num_cdf_customized_xaxis(\n",
+    "    data_seren[(data_seren[\"gpu_num\"] > 0) & (data_seren[\"type\"] == \"Eval\")], x_axis=x, key=\"duration\"\n",
+    ")\n",
+    "y_gpu_seren_tuning = calculate_num_cdf_customized_xaxis(\n",
+    "    data_seren[(data_seren[\"gpu_num\"] > 0) & (data_seren[\"type\"] == \"SFT\")], x_axis=x, key=\"duration\"\n",
+    ")\n",
+    "y_gpu_seren_mllm = calculate_num_cdf_customized_xaxis(\n",
+    "    data_seren[(data_seren[\"gpu_num\"] > 0) & (data_seren[\"type\"] == \"MLLM\")], x_axis=x, key=\"duration\"\n",
+    ")\n",
+    "\n",
+    "y_gpu_kalos_other = calculate_num_cdf_customized_xaxis(\n",
+    "    data_kalos[(data_kalos[\"gpu_num\"] > 0) & (data_kalos[\"type\"] == \"Other\")], x_axis=x, key=\"duration\"\n",
+    ")\n",
+    "y_gpu_kalos_debug = calculate_num_cdf_customized_xaxis(\n",
+    "    data_kalos[(data_kalos[\"gpu_num\"] > 0) & (data_kalos[\"type\"] == \"Debug\")], x_axis=x, key=\"duration\"\n",
+    ")\n",
+    "y_gpu_kalos_pretrain = calculate_num_cdf_customized_xaxis(\n",
+    "    data_kalos[(data_kalos[\"gpu_num\"] > 0) & (data_kalos[\"type\"] == \"Pretrain\")], x_axis=x, key=\"duration\"\n",
+    ")\n",
+    "y_gpu_kalos_eval = calculate_num_cdf_customized_xaxis(\n",
+    "    data_kalos[(data_kalos[\"gpu_num\"] > 0) & (data_kalos[\"type\"] == \"Eval\")], x_axis=x, key=\"duration\"\n",
+    ")\n",
+    "y_gpu_kalos_tuning = calculate_num_cdf_customized_xaxis(\n",
+    "    data_kalos[(data_kalos[\"gpu_num\"] > 0) & (data_kalos[\"type\"] == \"SFT\")], x_axis=x, key=\"duration\"\n",
+    ")\n",
+    "\n",
+    "# Queuing part\n",
+    "x2 = [2**i for i in range(0, 16)]\n",
+    "y_que_s_other = calculate_num_cdf_customized_xaxis(\n",
+    "    data_seren[(data_seren[\"gpu_num\"] > 0) & (data_seren[\"type\"] == \"Other\")], x_axis=x2, key=\"queue\"\n",
+    ")\n",
+    "y_que_s_debug = calculate_num_cdf_customized_xaxis(\n",
+    "    data_seren[(data_seren[\"gpu_num\"] > 0) & (data_seren[\"type\"] == \"Debug\")], x_axis=x2, key=\"queue\"\n",
+    ")\n",
+    "y_que_s_pretrain = calculate_num_cdf_customized_xaxis(\n",
+    "    data_seren[(data_seren[\"gpu_num\"] > 0) & (data_seren[\"type\"] == \"Pretrain\")], x_axis=x2, key=\"queue\"\n",
+    ")\n",
+    "y_que_s_eval = calculate_num_cdf_customized_xaxis(\n",
+    "    data_seren[(data_seren[\"gpu_num\"] > 0) & (data_seren[\"type\"] == \"Eval\")], x_axis=x2, key=\"queue\"\n",
+    ")\n",
+    "y_que_s_tuning = calculate_num_cdf_customized_xaxis(\n",
+    "    data_seren[(data_seren[\"gpu_num\"] > 0) & (data_seren[\"type\"] == \"SFT\")], x_axis=x2, key=\"queue\"\n",
+    ")\n",
+    "y_que_s_mllm = calculate_num_cdf_customized_xaxis(\n",
+    "    data_seren[(data_seren[\"gpu_num\"] > 0) & (data_seren[\"type\"] == \"MLLM\")], x_axis=x2, key=\"queue\"\n",
+    ")\n",
+    "\n",
+    "y_que_ali_other = calculate_num_cdf_customized_xaxis(\n",
+    "    data_kalos[(data_kalos[\"gpu_num\"] > 0) & (data_kalos[\"type\"] == \"Other\")], x_axis=x2, key=\"queue\"\n",
+    ")\n",
+    "y_que_ali_debug = calculate_num_cdf_customized_xaxis(\n",
+    "    data_kalos[(data_kalos[\"gpu_num\"] > 0) & (data_kalos[\"type\"] == \"Debug\")], x_axis=x2, key=\"queue\"\n",
+    ")\n",
+    "y_que_ali_pretrain = calculate_num_cdf_customized_xaxis(\n",
+    "    data_kalos[(data_kalos[\"gpu_num\"] > 0) & (data_kalos[\"type\"] == \"Pretrain\")], x_axis=x2, key=\"queue\"\n",
+    ")\n",
+    "y_que_ali_eval = calculate_num_cdf_customized_xaxis(\n",
+    "    data_kalos[(data_kalos[\"gpu_num\"] > 0) & (data_kalos[\"type\"] == \"Eval\")], x_axis=x2, key=\"queue\"\n",
+    ")\n",
+    "y_que_ali_tuning = calculate_num_cdf_customized_xaxis(\n",
+    "    data_kalos[(data_kalos[\"gpu_num\"] > 0) & (data_kalos[\"type\"] == \"SFT\")], x_axis=x2, key=\"queue\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "linestyles = [\"--\", \"-.\", \":\", \"--\", \"-.\", \":\"]\n",
+    "grid_params = dict(width_ratios=[1, 1])\n",
+    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(ncols=2, nrows=2, constrained_layout=True, figsize=(9, 7))\n",
+    "\n",
+    "# (a) Seren Duration\n",
+    "ax1.plot(x, y_gpu_seren_eval, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Evaluation\")\n",
+    "ax1.plot(x, y_gpu_seren_pretrain, linestyles[1], linewidth=3, alpha=0.9, color=cmp[1], label=\"Pretrain\")\n",
+    "ax1.plot(x, y_gpu_seren_tuning, linestyles[2], linewidth=3, alpha=0.9, color=cmp[2], label=\"SFT\")\n",
+    "ax1.plot(x, y_gpu_seren_mllm, linestyles[0], linewidth=3, alpha=0.9, color=cmp[3], label=\"MLLM\")\n",
+    "ax1.plot(x, y_gpu_seren_debug, linestyles[1], linewidth=3, alpha=0.9, color=cmp[4], label=\"Debug\")\n",
+    "ax1.plot(x, y_gpu_seren_other, linestyles[2], linewidth=3, alpha=0.9, color=cmp[5], label=\"Other\")\n",
+    "\n",
+    "\n",
+    "# (b) Seren Queuing\n",
+    "ax2.plot(x2, y_que_s_eval, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Evaluation\")\n",
+    "ax2.plot(x2, y_que_s_pretrain, linestyles[1], linewidth=3, alpha=0.9, color=cmp[1], label=\"Pretrain\")\n",
+    "ax2.plot(x2, y_que_s_tuning, linestyles[2], linewidth=3, alpha=0.9, color=cmp[2], label=\"SFT\")\n",
+    "ax2.plot(x2, y_que_s_mllm, linestyles[0], linewidth=3, alpha=0.9, color=cmp[3], label=\"MLLM\")\n",
+    "ax2.plot(x2, y_que_s_debug, linestyles[1], linewidth=3, alpha=0.9, color=cmp[4], label=\"Debug\")\n",
+    "ax2.plot(x2, y_que_s_other, linestyles[2], linewidth=3, alpha=0.9, color=cmp[5], label=\"Other\")\n",
+    "\n",
+    "# (c) Kalos Duration\n",
+    "ax3.plot(x, y_gpu_kalos_eval, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Evaluation\")\n",
+    "ax3.plot(x, y_gpu_kalos_pretrain, linestyles[1], linewidth=3, alpha=0.9, color=cmp[1], label=\"Pretrain\")\n",
+    "ax3.plot(x, y_gpu_kalos_debug, linestyles[1], linewidth=3, alpha=0.9, color=cmp[4], label=\"Debug\")\n",
+    "ax3.plot(x, y_gpu_kalos_other, linestyles[2], linewidth=3, alpha=0.9, color=cmp[5], label=\"Other\")\n",
+    "\n",
+    "\n",
+    "# (d) Kalos Queuing\n",
+    "ax4.plot(x2, y_que_ali_eval, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Evaluation\")\n",
+    "ax4.plot(x2, y_que_ali_pretrain, linestyles[1], linewidth=3, alpha=0.9, color=cmp[1], label=\"Pretrain\")\n",
+    "ax4.plot(x2, y_que_ali_debug, linestyles[1], linewidth=3, alpha=0.9, color=cmp[4], label=\"Debug\")\n",
+    "ax4.plot(x2, y_que_ali_other, linestyles[2], linewidth=3, alpha=0.9, color=cmp[5], label=\"Other\")\n",
+    "\n",
+    "ax1.set_xlabel(f\"(a) Job Duration (s)\")\n",
+    "ax1.set_ylabel(f\"CDF (%)\")\n",
+    "ax1.set_xscale(\"log\")\n",
+    "ax1.set_xticks([1e0, 1e1, 1e2, 1e3, 1e4, 1e5, 1e6])\n",
+    "ax1.set_xlim(1, x[-1])\n",
+    "ax1.set_ylim(-0.5, 100.8)\n",
+    "handles, labels = ax1.get_legend_handles_labels()\n",
+    "fig.legend(handles=handles, labels=labels, ncols=6, bbox_to_anchor=(-0.01, 1.08), loc=2, columnspacing=0.9, handletextpad=0.2)\n",
+    "ax1.grid(linestyle=\":\")\n",
+    "\n",
+    "ax2.set_xlabel(f\"(b) Job Queuing Delay (s)\")\n",
+    "ax2.set_ylabel(f\"CDF (%)\")\n",
+    "ax2.set_xscale(\"log\")\n",
+    "ax2.set_xticks([1e0, 1e1, 1e2, 1e3, 1e4])\n",
+    "ax2.set_xlim(1, x2[-1])\n",
+    "ax2.set_ylim(-0.5, 100.8)\n",
+    "ax2.grid(linestyle=\":\")\n",
+    "\n",
+    "ax3.set_xlabel(f\"(c) Job Duration (s)\")\n",
+    "ax3.set_ylabel(f\"CDF (%)\")\n",
+    "ax3.set_xscale(\"log\")\n",
+    "ax3.set_xticks([1e0, 1e1, 1e2, 1e3, 1e4, 1e5, 1e6])\n",
+    "ax3.set_xlim(1, x[-1])\n",
+    "ax3.set_ylim(-0.5, 100.8)\n",
+    "ax3.grid(linestyle=\":\")\n",
+    "\n",
+    "ax4.set_xlabel(f\"(d) Job Queuing Delay (s)\")\n",
+    "ax4.set_ylabel(f\"CDF (%)\")\n",
+    "ax4.set_xscale(\"log\")\n",
+    "ax4.set_xticks([1e0, 1e1, 1e2, 1e3, 1e4])\n",
+    "ax4.set_xlim(1, x2[-1])\n",
+    "ax4.set_ylim(-0.5, 100.8)\n",
+    "ax4.grid(linestyle=\":\")\n",
+    "\n",
+    "# 1 hour and 1 day\n",
+    "ax1.axvline(x=3600, ls=\"--\", alpha=0.6, c=\"gray\", ymax=0.94, lw=1.5)\n",
+    "ax1.axvline(x=3600 * 24, ls=\"--\", alpha=0.9, c=\"gray\", ymax=0.94, lw=1.5)\n",
+    "ax3.axvline(x=3600, ls=\"--\", alpha=0.6, c=\"gray\", ymax=0.94, lw=1.5)\n",
+    "ax3.axvline(x=3600 * 24, ls=\"--\", alpha=0.9, c=\"gray\", ymax=0.94, lw=1.5)\n",
+    "\n",
+    "sns.despine()\n",
+    "ax1.text(0.78, 0.03, \"Seren\", transform=ax1.transAxes, size=20, fontweight=\"bold\")\n",
+    "ax2.text(0.78, 0.03, \"Seren\", transform=ax2.transAxes, size=20, fontweight=\"bold\")\n",
+    "ax3.text(0.78, 0.03, \"Kalos\", transform=ax3.transAxes, size=20, fontweight=\"bold\")\n",
+    "ax4.text(0.78, 0.03, \"Kalos\", transform=ax4.transAxes, size=20, fontweight=\"bold\")\n",
+    "\n",
+    "fig.savefig(f\"{SAVEPATH}/cdf_job_duration_queue.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Box Plot: Request GPU number Different Type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cmap = sns.color_palette(\"pastel\")\n",
+    "fig, (ax1, ax2) = plt.subplots(\n",
+    "    ncols=2,\n",
+    "    nrows=1,\n",
+    "    gridspec_kw={\"width_ratios\": [4.2, 3]},\n",
+    "    constrained_layout=True,\n",
+    "    figsize=(9, 3.75),\n",
+    ")\n",
+    "\n",
+    "############ Fig 1 ############\n",
+    "data_seren.sort_values(by=\"gpu_num\", ascending=False, inplace=True)\n",
+    "data_seren[\"type\"].replace(\"SFT\", \"SFT\", inplace=True)\n",
+    "\n",
+    "x_ticks = [\n",
+    "    \"Eval\",\n",
+    "    \"Pretrain\",\n",
+    "    \"SFT\",\n",
+    "    \"MLLM\",\n",
+    "    \"Debug\",\n",
+    "    \"Other\",\n",
+    "]\n",
+    "\n",
+    "flierprops = dict(marker=\".\", markerfacecolor=\"k\", markersize=2, linestyle=\"none\")\n",
+    "sns.boxplot(\n",
+    "    x=\"type\",\n",
+    "    y=\"gpu_num\",\n",
+    "    data=data_seren,\n",
+    "    flierprops=flierprops,\n",
+    "    width=0.6,\n",
+    "    linewidth=2.2,\n",
+    "    saturation=2,\n",
+    "    palette=cmap,\n",
+    "    ax=ax1,\n",
+    "    order=x_ticks,\n",
+    "    boxprops=dict(alpha=1),\n",
+    ")\n",
+    "sns.color_palette(\"tab10\")\n",
+    "ax1.set_xlabel(\"(a) Seren\")\n",
+    "ax1.set_xticklabels(ax1.get_xticklabels(), rotation=0)\n",
+    "ax1.set_ylabel(f\"Number of GPUs\")\n",
+    "ax1.set_yscale(\"log\")\n",
+    "ax1.grid(axis=\"y\", linestyle=\":\")\n",
+    "\n",
+    "\n",
+    "############ Fig 2 ############\n",
+    "data_kalos.sort_values(by=\"gpu_num\", ascending=False, inplace=True)\n",
+    "data_kalos = data_kalos[data_kalos[\"type\"] != \"SFT\"]\n",
+    "x_ticks_k = [\n",
+    "    \"Eval\",\n",
+    "    \"Pretrain\",\n",
+    "    \"Debug\",\n",
+    "    \"Other\",\n",
+    "]\n",
+    "my_pal = [cmap[0], cmap[1], cmap[4], cmap[5]]\n",
+    "\n",
+    "flierprops = dict(marker=\".\", markerfacecolor=\"k\", markersize=3, linestyle=\"none\")\n",
+    "sns.boxplot(\n",
+    "    x=\"type\",\n",
+    "    y=\"gpu_num\",\n",
+    "    data=data_kalos,\n",
+    "    flierprops=flierprops,\n",
+    "    width=0.6,\n",
+    "    linewidth=2.2,\n",
+    "    saturation=2,\n",
+    "    palette=my_pal,\n",
+    "    ax=ax2,\n",
+    "    order=x_ticks_k,\n",
+    "    boxprops=dict(alpha=1),\n",
+    ")\n",
+    "sns.color_palette(\"tab10\")\n",
+    "ax2.set_xlabel(\"(b) Kalos\")\n",
+    "ax2.set_ylabel(None)\n",
+    "ax2.set_xticklabels(ax2.get_xticklabels(), rotation=0)\n",
+    "ax2.set_yscale(\"log\")\n",
+    "ax2.grid(axis=\"y\", linestyle=\":\")\n",
+    "\n",
+    "sns.despine()\n",
+    "fig.savefig(f\"{SAVEPATH}/box_gpu_num.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CDF: Resource Utilization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(f\"{PKLPATH}/util_gpu_seren.pkl\", \"rb\") as file:\n",
+    "    _, _, x2, y2, x3, y3, x4, y4, x5, y5 = pickle.load(file)\n",
+    "with open(f\"{PKLPATH}/util_gpu_kalos_full.pkl\", \"rb\") as file:\n",
+    "    _, _, x2_k, y2_k, x3_k, y3_k, x4_k, y4_k, x5_k, y5_k = pickle.load(file)\n",
+    "with open(f\"{PKLPATH}/util_cpu_mem_seren.pkl\", \"rb\") as file:\n",
+    "    x6, y6, x7, y7 = pickle.load(file)\n",
+    "with open(f\"{PKLPATH}/util_cpu_mem_kalos.pkl\", \"rb\") as file:\n",
+    "    x6_k, y6_k, x7_k, y7_k = pickle.load(file)\n",
+    "with open(f\"{PKLPATH}/ib_seren.pkl\", \"rb\") as file:\n",
+    "    x8, y8, x9, y9 = pickle.load(file)\n",
+    "\n",
+    "x8 = x8 / x8.max() * 100\n",
+    "x9 = x9 / x9.max() * 100\n",
+    "\n",
+    "linestyles = [\"--\", \":\", \"--\", \"-.\", \":\"]\n",
+    "grid_params = dict(width_ratios=[1, 1])\n",
+    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(ncols=2, nrows=2, constrained_layout=True, figsize=(9, 7))\n",
+    "\n",
+    "############ Fig 1: SM, Occupancy ############\n",
+    "ax1.plot(x3, y3, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Seren SM Activity\")\n",
+    "ax1.plot(x5, y5, linestyles[1], linewidth=3, alpha=0.9, color=cmp[0], label=\"Seren Occupancy\")\n",
+    "ax1.plot(x3_k, y3_k, linestyles[0], linewidth=3, alpha=0.9, color=cmp[1], label=\"Kalos SM Activity\")\n",
+    "ax1.plot(x5_k, y5_k, linestyles[1], linewidth=3, alpha=0.9, color=cmp[1], label=\"Kalos Occupancy\")\n",
+    "\n",
+    "############ Fig 2: CPU mem usage, GPU mem usage ############\n",
+    "ax2.plot(x7, y7, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Seren CPU Mem\")\n",
+    "ax2.plot(x2, y2, linestyles[1], linewidth=3, alpha=0.9, color=cmp[0], label=\"Seren GPU Mem\")\n",
+    "ax2.plot(x7_k, y7_k, linestyles[0], linewidth=3, alpha=0.9, color=cmp[1], label=\"Kalos CPU Mem\")\n",
+    "ax2.plot(x2_k, y2_k, linestyles[1], linewidth=3, alpha=0.9, color=cmp[1], label=\"Kalos GPU Mem\")\n",
+    "\n",
+    "############ Fig 3: CPU util ############\n",
+    "ax3.plot(x6, y6, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Seren\")\n",
+    "ax3.plot(x6_k, y6_k, linestyles[0], linewidth=3, alpha=0.9, color=cmp[1], label=\"Kalos\")\n",
+    "\n",
+    "############ Fig 4: IB send, receive ############\n",
+    "ax4.plot(x8, y8, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"IB Send\")\n",
+    "ax4.plot(x9, y9, linestyles[1], linewidth=3, alpha=0.9, color=cmp[0], label=\"IB Receive\")\n",
+    "\n",
+    "ax1.set_xlabel(f\"(a) GPU DCGM Metric (%)\")\n",
+    "ax1.set_ylabel(f\"CDF (%)\")\n",
+    "ax1.set_xlim(-0.8, 100.8)\n",
+    "ax1.set_ylim(0, 100.8)\n",
+    "ax1.set_xticks([0, 25, 50, 75, 100])\n",
+    "ax1.grid(linestyle=\":\")\n",
+    "\n",
+    "ax2.set_xlabel(f\"(b) Memory Footprint (%)\")\n",
+    "ax2.set_ylabel(f\"CDF (%)\")\n",
+    "ax2.set_xlim(-0.8, 100.8)\n",
+    "ax2.set_xticks([0, 25, 50, 75, 100])\n",
+    "ax2.set_ylim(0, 100.8)\n",
+    "ax2.grid(linestyle=\":\")\n",
+    "\n",
+    "ax3.set_xlabel(f\"(c) CPU Utilization (%)\")\n",
+    "ax3.set_ylabel(f\"CDF (%)\")\n",
+    "ax3.set_xlim(-0.8, 100.8)\n",
+    "ax3.set_xticks([0, 25, 50, 75, 100])\n",
+    "ax3.set_ylim(0, 100.8)\n",
+    "ax3.legend(loc=\"lower right\")\n",
+    "ax3.grid(linestyle=\":\")\n",
+    "\n",
+    "ax4.set_xlabel(f\"(d) Network (%)\")\n",
+    "ax4.set_ylabel(f\"CDF (%)\")\n",
+    "ax4.set_xlim(-0.8, 100.8)\n",
+    "ax4.set_xticks([0, 25, 50, 75, 100])\n",
+    "ax4.set_ylim(0, 100.8)\n",
+    "ax4.legend(loc=\"lower right\")\n",
+    "ax4.grid(linestyle=\":\")\n",
+    "sns.despine()\n",
+    "\n",
+    "\n",
+    "S = mpatches.Patch(facecolor=cmp[0], alpha=0.9)\n",
+    "K = mpatches.Patch(facecolor=cmp[1], alpha=0.9)\n",
+    "A = (Line2D([0], [0], color=\"black\", lw=3, ls=\"--\"),)\n",
+    "B = (Line2D([0], [0], color=\"black\", lw=3, ls=\":\"),)\n",
+    "\n",
+    "legend1 = ax1.legend([S, K], [\"Seren\", \"Kalos\"], bbox_to_anchor=(0.5, 0.62), loc=2, ncol=1, fontsize=17, frameon=False)\n",
+    "\n",
+    "ax1.add_artist(legend1)\n",
+    "\n",
+    "ax1.legend([A, B], [\"SM Activity\", \"TC Activity\"], bbox_to_anchor=(0.3, 0.36), loc=2, ncol=1)\n",
+    "\n",
+    "ax2.legend([A, B], [\"CPU Memory\", \"GPU Memory\"], bbox_to_anchor=(0.25, 0.36), loc=2, ncol=1)\n",
+    "\n",
+    "fig.savefig(f\"{SAVEPATH}/cdf_resource_util.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CDF: Temperature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We use August data for GPU temperature and power\n",
+    "with open(f\"{PKLPATH}/gpu_temp_seren.pkl\", \"rb\") as file:\n",
+    "    x, y1, x2, y2 = pickle.load(file)\n",
+    "with open(f\"{PKLPATH}/gpu_temp_kalos.pkl\", \"rb\") as file:\n",
+    "    x1_k, y1_k, x2_k, y2_k = pickle.load(file)\n",
+    "with open(f\"{PKLPATH}/gpu_power_seren.pkl\", \"rb\") as file:\n",
+    "    x3, y3 = pickle.load(file)\n",
+    "with open(f\"{PKLPATH}/gpu_power_kalos.pkl\", \"rb\") as file:\n",
+    "    x3_k, y3_k = pickle.load(file)\n",
+    "\n",
+    "linestyles = [\"-\", \":\", \":\", \"-\"]\n",
+    "fig, ax1 = plt.subplots(ncols=1, nrows=1, constrained_layout=True, figsize=(5, 3.75))\n",
+    "\n",
+    "############ Fig 1: Temperature ############\n",
+    "ax1.plot(x, y1, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Seren GPU Temp\")\n",
+    "ax1.plot(x2, y2, linestyles[1], linewidth=3, alpha=0.9, color=cmp[0], label=\"Seren GPU Mem Temp\")\n",
+    "ax1.plot(x1_k, y1_k, linestyles[0], linewidth=3, alpha=0.9, color=cmp[1], label=\"Kalos GPU Temp\")\n",
+    "ax1.plot(x2_k, y2_k, linestyles[1], linewidth=3, alpha=0.9, color=cmp[1], label=\"Kalos GPU Mem Temp\")\n",
+    "\n",
+    "ax1.set_xlabel(f\"Temperature (°C)\")\n",
+    "ax1.set_ylabel(f\"CDF (%)\")\n",
+    "ax1.set_xlim(20, 85)\n",
+    "ax1.set_ylim(0, 100.8)\n",
+    "ax1.grid(linestyle=\":\")\n",
+    "\n",
+    "S = mpatches.Patch(facecolor=cmp[0], alpha=0.9)\n",
+    "K = mpatches.Patch(facecolor=cmp[1], alpha=0.9)\n",
+    "A = (Line2D([0], [0], color=\"black\", lw=3, ls=\"-\"),)\n",
+    "B = (Line2D([0], [0], color=\"black\", lw=3, ls=\":\"),)\n",
+    "\n",
+    "legend1 = ax1.legend([S, K], [\"Seren\", \"Kalos\"], bbox_to_anchor=(0.6, 0.62), loc=2, ncol=1, fontsize=17, frameon=False)\n",
+    "\n",
+    "ax1.add_artist(legend1)\n",
+    "\n",
+    "ax1.legend(\n",
+    "    [A, B],\n",
+    "    [\"GPU Temp.\", \"GMem Temp.\"],\n",
+    "    bbox_to_anchor=(0.4, 0.32),\n",
+    "    loc=2,\n",
+    "    ncol=1,\n",
+    "    fontsize=17,\n",
+    ")\n",
+    "\n",
+    "sns.despine()\n",
+    "fig.savefig(f\"{SAVEPATH}/cdf_temperature.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CDF: Power"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(f\"{PKLPATH}/server_power.pkl\", \"rb\") as file:\n",
+    "    x1, y1, x2, y2 = pickle.load(file)\n",
+    "with open(f\"{PKLPATH}/gpu_power_seren.pkl\", \"rb\") as file:\n",
+    "    x3, y3 = pickle.load(file)\n",
+    "with open(f\"{PKLPATH}/gpu_power_kalos.pkl\", \"rb\") as file:\n",
+    "    x3_k, y3_k = pickle.load(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "linestyles = [\"--\", \":\", \":\", \"-\"]\n",
+    "grid_params = dict(width_ratios=[1, 1])\n",
+    "fig, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, constrained_layout=True, figsize=(9, 3.75))\n",
+    "\n",
+    "############ Fig 1: GPU power ############\n",
+    "ax1.plot(x3, y3, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"Seren\")\n",
+    "ax1.plot(x3_k, y3_k, linestyles[0], linewidth=3, alpha=0.9, color=cmp[1], label=\"Kalos\")\n",
+    "ax1.axvline(x=400, ls=\"--\", alpha=0.6, c=\"gray\", ymax=100, lw=1.5)\n",
+    "ax1.annotate(\n",
+    "    \"A100 TDP\",\n",
+    "    xy=(400, 33),\n",
+    "    xytext=(420, 20),\n",
+    "    arrowprops=dict(facecolor=\"black\", width=2.5, headwidth=8),\n",
+    "    color=\"black\",\n",
+    "    fontsize=16,\n",
+    ")\n",
+    "ax1.annotate(\n",
+    "    \"Max=600\",\n",
+    "    xy=(600, 100),\n",
+    "    xytext=(430, 85),\n",
+    "    arrowprops=dict(facecolor=\"black\", width=2.5, headwidth=8),\n",
+    "    color=\"black\",\n",
+    "    fontsize=16,\n",
+    ")\n",
+    "\n",
+    "############ Fig 2: Server power ############\n",
+    "ax2.plot(x1, y1, linestyles[0], linewidth=3, alpha=0.9, color=cmp[0], label=\"GPU Node\")\n",
+    "ax2.plot(x2, y2, linestyles[1], linewidth=3, alpha=0.9, color=cmp[0], label=\"CPU Node\")\n",
+    "ax2.annotate(\n",
+    "    \"Max=960\",\n",
+    "    xy=(960, 100),\n",
+    "    xytext=(1200, 90),\n",
+    "    arrowprops=dict(facecolor=\"black\", width=2.5, headwidth=8),\n",
+    "    color=\"black\",\n",
+    "    fontsize=16,\n",
+    ")\n",
+    "ax2.annotate(\n",
+    "    \"Max=6550\",\n",
+    "    xy=(6550, 100),\n",
+    "    xytext=(4500, 70),\n",
+    "    arrowprops=dict(facecolor=\"black\", width=2.5, headwidth=8),\n",
+    "    color=\"black\",\n",
+    "    fontsize=16,\n",
+    ")\n",
+    "\n",
+    "ax1.set_xlabel(f\"(a) GPU Power (W)\")\n",
+    "ax1.set_ylabel(f\"CDF (%)\")\n",
+    "ax1.set_xlim(-0.8, 610)\n",
+    "ax1.set_ylim(0, 100.8)\n",
+    "ax1.legend()\n",
+    "ax1.grid(linestyle=\":\")\n",
+    "ax1.xaxis.set_minor_locator(matplotlib.ticker.FixedLocator([60]))\n",
+    "ax1.xaxis.set_minor_formatter(matplotlib.ticker.FixedFormatter([60]))\n",
+    "ax1.tick_params(axis=\"x\", which=\"minor\", labelsize=15)\n",
+    "\n",
+    "ax2.set_xlabel(f\"(b) Server Power in Seren (W)\")\n",
+    "ax2.set_ylabel(f\"CDF (%)\")\n",
+    "ax2.set_xlim(-0.8, x1.max())\n",
+    "ax2.set_ylim(0, 100.8)\n",
+    "ax2.legend(loc=\"lower right\")\n",
+    "ax2.grid(linestyle=\":\")\n",
+    "ax2.xaxis.set_minor_locator(matplotlib.ticker.FixedLocator([520]))\n",
+    "ax2.xaxis.set_minor_formatter(matplotlib.ticker.FixedFormatter([520]))\n",
+    "ax2.tick_params(axis=\"x\", which=\"minor\", labelsize=15)\n",
+    "sns.despine()\n",
+    "\n",
+    "fig.savefig(f\"{SAVEPATH}/cdf_power.pdf\", bbox_inches=\"tight\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

data/cluster_summary.csv

@@ -0,0 +1,6 @@
+id,job_num,cpu_job_num,gpu_job_num,avg_run_time_gpu,avg_que_time_gpu,avg_gpu_num,med_run_time_gpu,med_que_time_gpu,med_gpu_num,max_run_time_gpu,max_gpu,complete_rate_gpu,cancel_rate_gpu,fail_rate_gpu,complete_gpu_time,cancel_gpu_time,fail_gpu_time,complete_rate_gpu_time,cancel_rate_gpu_time,fail_rate_gpu_time,avg_run_time_cpu,avg_que_time_cpu,med_run_time_cpu,med_que_time_cpu,complete_rate_cpu,cancel_rate_cpu,fail_rate_cpu
+Seren,1031550,367737,663813,1414.335,445.984,5.68,122.0,1.0,1.0,1209604.0,1024.0,0.497,0.075,0.429,4560592618.0,14274584510.0,2675447459.0,0.212,0.664,0.124,733.894,4.813,14.0,1.0,0.858,0.039,0.102
+Kalos,62413,42506,19907,1259.689,214.179,26.77,124.0,17.0,1.0,625740.0,1024.0,0.541,0.062,0.397,1667685146.0,3239202064.0,426292851.0,0.313,0.607,0.08,118.352,88.401,22.0,9.0,0.86,0.001,0.14
+Philly,112956,0,112956,18006.055,3760.604,1.928,874.0,189.0,1.0,4240676.0,128.0,0.604,0.09,0.305,2210176414.0,2025326056.0,2659215061.0,0.321,0.294,0.386,,,,,,,
+Helios,3362981,1782517,1580464,6651.681,862.047,3.716,206.0,0.0,1.0,4320009.0,2048.0,0.624,0.221,0.155,39994296320.0,30715109125.0,7288334702.0,0.513,0.394,0.093,628.759,73.907,2.0,0.0,0.909,0.03,0.061
+PAI,1260920,0,1037085,4787.109,401.742,0.683,480.667,9.0,0.5,626371.0,8.0,,,,,,,,,,,,,,,,

data/generate_utilization_pkl.ipynb

@@ -0,0 +1,292 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Process Utilization Files and Generate a Pickle Files for Plotting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Note that running following scripts is time consuming and should be done only once. The pickle files are provided in the `util_pkl` directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pytz\n",
+    "import pickle\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "from glob import glob\n",
+    "from pathlib import Path\n",
+    "from utils import cluster_metric_header, dcgm_metric_header\n",
+    "\n",
+    "\"\"\" cluster_metric_header\n",
+    "0: \"CPU\",\n",
+    "1: \"MEMORY\",\n",
+    "2: \"IB_SEND\",\n",
+    "3: \"RECEIVE\",\n",
+    "\"\"\"\n",
+    "\"\"\" dcgm_metric_header\n",
+    "0: XID_ERRORS\n",
+    "1: GPU_TEMP\n",
+    "2: MEMORY_TEMP\n",
+    "3: MEM_CLOCK\n",
+    "4: MEM_COPY_UTIL\n",
+    "5: FB_FREE\n",
+    "6: FB_USED\n",
+    "7: DRAM_ACTIVE\n",
+    "8: POWER_USAGE\n",
+    "9: GPU_UTIL\n",
+    "10: PIPE_TENSOR_ACTIVE\n",
+    "11: SM_ACTIVE\n",
+    "12: SM_OCCUPANCY\n",
+    "\"\"\"\n",
+    "\n",
+    "SAVEPKL = \"./util_pkl\"\n",
+    "\n",
+    "\n",
+    "def read_csv_with_concat(path=\"./csv\", file_name=None):\n",
+    "    file = Path(path, f\"{file_name}.csv\")\n",
+    "\n",
+    "    if file.exists():\n",
+    "        # If original file exists, read it directly\n",
+    "        df = pd.read_csv(file)\n",
+    "        print(f\"Reading {file_name}\")\n",
+    "    else:\n",
+    "        # If original file does not exist, read all the split files\n",
+    "        split_files = sorted(glob(f\"{path}/{file_name}-2023-*.csv\"))\n",
+    "        print(f\"Reading splitted files: {split_files}\")\n",
+    "        df = pd.concat([pd.read_csv(split_file) for split_file in split_files])\n",
+    "        df.reset_index(drop=True, inplace=True)\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def read_concat_parse_save_cluster_metrics(path=\"./csv_cpu\", metrics=cluster_metric_header):\n",
+    "    for metric in metrics:\n",
+    "        data = read_csv_with_concat(path=path, file_name=metric)\n",
+    "        data.drop_duplicates(subset=[\"Time\"], inplace=True)\n",
+    "        data.sort_values(by=\"Time\", inplace=True)\n",
+    "        data[\"Time\"] = pd.to_datetime(data[\"Time\"], unit=\"s\").dt.tz_localize(pytz.utc).dt.tz_convert(\"Asia/Shanghai\")\n",
+    "        data.set_index(\"Time\", drop=True, inplace=True)\n",
+    "        print(f\"Column Number: {len(list(data.columns))}, {len(set(list(data.columns)))}\")\n",
+    "\n",
+    "        if \"NODE_MEMORY\" in metric:\n",
+    "            # Around 2 hours has some bug (ip has additional '.1', like '10.140.0.131' -> '10.140.0.131.1')\n",
+    "            data = data[(data.index < \"2023-07-19 11:35:00\") | (data.index > \"2023-07-19 14:01:00\")]\n",
+    "\n",
+    "        if \"NODE_CPU\" in metric or \"NODE_MEMORY\" in metric:\n",
+    "            data = data * 100  # CPU / Memory Utilization (%)\n",
+    "\n",
+    "        if \"NODE_IB\" in metric:\n",
+    "            data.rename(columns=lambda x: x.replace(\"-mlx5_0\", \"\"), inplace=True)  # Simplified, since one IB NIC per server\n",
+    "\n",
+    "        data.dropna(axis=1, how=\"all\", inplace=True)\n",
+    "        data = data.round(3)\n",
+    "        data.to_csv(f\"./{metric}.csv\")\n",
+    "\n",
+    "\n",
+    "def read_concat_parse_save_dcgm_metrics(path=\"./csv\", metrics=dcgm_metric_header):\n",
+    "    for metric in metrics:\n",
+    "        data = read_csv_with_concat(path=path, file_name=metric)\n",
+    "        data.drop_duplicates(subset=[\"Time\"], inplace=True)\n",
+    "        data.sort_values(by=\"Time\", inplace=True)\n",
+    "        data[\"Time\"] = pd.to_datetime(data[\"Time\"], unit=\"s\").dt.tz_localize(pytz.utc).dt.tz_convert(\"Asia/Shanghai\")\n",
+    "        data.set_index(\"Time\", drop=True, inplace=True)\n",
+    "        print(f\"Column Number: {len(list(data.columns))}, {len(set(list(data.columns)))}\")\n",
+    "\n",
+    "        # if \"XID\" in metric or \"TEMP\" in metric or \"CLOC\" in metric:\n",
+    "        #     data = data.astype(int, errors='ignore')\n",
+    "\n",
+    "        if \"ACTIVE\" in metric or \"OCCUPANCY\" in metric:\n",
+    "            data = data * 100  # CPU / Memory Utilization (%)\n",
+    "            data = data.round(3)\n",
+    "\n",
+    "        if \"POWER\" in metric:\n",
+    "            data = data.round(1)\n",
+    "\n",
+    "        data.dropna(axis=0, how=\"all\", inplace=True)\n",
+    "        data.dropna(axis=1, how=\"all\", inplace=True)\n",
+    "        data.to_csv(f\"./{metric}.csv\")\n",
+    "\n",
+    "\n",
+    "def calculate_sum_cdf_axis100(df, dot_num=1000):\n",
+    "    \"\"\"\n",
+    "    Calculate quantity percentile CDF, y-axis: 0-100%,\n",
+    "    \"\"\"\n",
+    "    print(\"Parsing\")\n",
+    "    data = df.melt(id_vars=\"Time\", var_name=\"Server\")\n",
+    "    data.dropna(subset=[\"value\"], inplace=True)\n",
+    "\n",
+    "    y = np.linspace(0, 1, num=dot_num)\n",
+    "    x = data[\"value\"].quantile(y).values\n",
+    "    y = y * 100\n",
+    "    return x, y\n",
+    "\n",
+    "\n",
+    "def calculate_num_cdf_axis100(df, dot_num=1000):\n",
+    "    \"\"\"\n",
+    "    Calculate quantity percentile CDF, y-axis: 0-100%,\n",
+    "    \"\"\"\n",
+    "    print(\"Parsing\")\n",
+    "    data = df.melt(id_vars=\"Time\", var_name=\"Server\")\n",
+    "    data.dropna(subset=[\"value\"], inplace=True)\n",
+    "    # data.sort_values('value', ascending=True, inplace=True)\n",
+    "    # data.reset_index(drop=True, inplace=True)\n",
+    "\n",
+    "    y = np.linspace(0, 1, num=dot_num)\n",
+    "    x = data[\"value\"].quantile(y).values\n",
+    "    y = y * 100\n",
+    "    return x, y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example 1: Prometheus Metics (e.g., CPU and Memory Utilization)\n",
+    "\n",
+    "You can change to any metric you want to plot by changing the `file_name` variable in the following script."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_cpu = read_csv_with_concat(path=\"./seren\", file_name=cluster_metric_header[0])\n",
+    "data_mem = read_csv_with_concat(path=\"./seren\", file_name=cluster_metric_header[1])\n",
+    "x1, y1 = calculate_num_cdf_axis100(data_cpu)\n",
+    "x2, y2 = calculate_num_cdf_axis100(data_mem)\n",
+    "print(\n",
+    "    f'CPU Period: (Start) {data_cpu.at[0, \"Time\"].split(\":\")[0]}h (End) {data_cpu.at[len(data_cpu)-1, \"Time\"].split(\":\")[0]}h'\n",
+    ")\n",
+    "print(\n",
+    "    f'MEM Period: (Start) {data_mem.at[0, \"Time\"].split(\":\")[0]}h (End) {data_mem.at[len(data_mem)-1, \"Time\"].split(\":\")[0]}h'\n",
+    ")\n",
+    "\n",
+    "with open(f\"{SAVEPKL}/util_cpu_mem_seren.pkl\", \"wb\") as file:\n",
+    "    pickle.dump([x1, y1, x2, y2], file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example 2: NVIDIA DCGM Metics (e.g., GPU and GPU Memory Utilization)\n",
+    "\n",
+    "You can change to any metric you want to plot by changing the `file_name` variable in the following script."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_gpu_util = read_csv_with_concat(path=\"./seren\", file_name=dcgm_metric_header[9])  # \"DCGM_FI_DEV_GPU_UTIL\"\n",
+    "data_gpu_mem = read_csv_with_concat(path=\"./seren\", file_name=dcgm_metric_header[6])  # \"DCGM_FI_DEV_FB_USED\"\n",
+    "data_gpu_mem.iloc[:, 1:] = 100 * data_gpu_mem.iloc[:, 1:] / (80 * 1024)\n",
+    "x1, y1 = calculate_num_cdf_axis100(data_gpu_util)\n",
+    "x2, y2 = calculate_num_cdf_axis100(data_gpu_mem)\n",
+    "\n",
+    "with open(f\"{SAVEPKL}/util_gpu_util_mem_seren.pkl\", \"wb\") as file:\n",
+    "    pickle.dump([x1, y1, x2, y2], file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Processing IPMI Power Files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_AB = pd.read_csv(\"./ipmi/GPU_AB_Power.csv\", parse_dates=[\"Time\"])\n",
+    "df_C = pd.read_csv(\"./ipmi/GPU_C_Power.csv\", parse_dates=[\"Time\"])\n",
+    "df_D = pd.read_csv(\"./ipmi/CPU_D_Power.csv\", parse_dates=[\"Time\"])\n",
+    "\n",
+    "df_A = df_AB.dropna()\n",
+    "df_B = df_AB[df_AB.isna().any(axis=1)]  # Type B without MEM_Power record\n",
+    "\n",
+    "dfs = {\"GPU_A\": df_A, \"GPU_B\": df_B, \"GPU_C\": df_C, \"CPU_D\": df_D}\n",
+    "\n",
+    "# Extract sys_total_power\n",
+    "df_A_power = df_A[[\"Time\", \"Sys_Total_Power\"]]\n",
+    "df_B_power = df_B[[\"Time\", \"Sys_Total_Power\"]]\n",
+    "df_C_power = df_C[[\"Time\", \"Sys_Total_Power\"]]\n",
+    "df_gpu = pd.concat([df_A_power, df_B_power, df_C_power])\n",
+    "\n",
+    "x1, y1 = calculate_sum_cdf_axis100(df_gpu)\n",
+    "x2, y2 = calculate_sum_cdf_axis100(df_D[[\"Time\", \"Sys_Total_Power\"]])\n",
+    "\n",
+    "with open(f\"./server_power.pkl\", \"wb\") as file:\n",
+    "    pickle.dump([x1, y1, x2, y2], file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Processing Philly GPU Utilization Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "philly = pd.read_csv(\n",
+    "    \"./philly/philly_gpu_util.csv\", on_bad_lines=\"skip\", header=0\n",
+    ")  # Please refer to their official repo for the data\n",
+    "cols = list(philly.columns)\n",
+    "philly = philly.drop(columns=[cols[-1]])\n",
+    "philly.reset_index(inplace=True)\n",
+    "philly.columns = cols\n",
+    "philly.rename(columns={\"time\": \"Time\"}, inplace=True)\n",
+    "philly = philly.drop(columns=[cols[1]])\n",
+    "\n",
+    "x1, y1 = calculate_num_cdf_axis100(philly)\n",
+    "with open(f\"{SAVEPKL}/util_gpu_util_philly.pkl\", \"wb\") as file:\n",
+    "    pickle.dump([x1, y1], file)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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+size 201182363

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data/utils.py

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+cluster_metric_header = [
+    "NODE_CPU_UTILIZATION",
+    "NODE_MEMORY_UTILIZATION",
+    "NODE_IB_SEND",
+    "NODE_IB_RECEIVE",
+]
+
+dcgm_metric_header = [
+    "XID_ERRORS",
+    "GPU_TEMP",
+    "MEMORY_TEMP",
+    "MEM_CLOCK",
+    "MEM_COPY_UTIL",
+    "FB_FREE",
+    "FB_USED",
+    "DRAM_ACTIVE",
+    "POWER_USAGE",
+    "GPU_UTIL",
+    "PIPE_TENSOR_ACTIVE",
+    "SM_ACTIVE",
+    "SM_OCCUPANCY",
+]
+
+dcgm_full = [
+    "DCGM_FI_DEV_CORRECTABLE_REMAPPED_ROWS",  # 可纠正错误的重新映射行数 Number of remapped rows for correctable errors.
+    "DCGM_FI_DEV_DEC_UTIL",  # GPU 解码器利用率（单位：%）
+    "DCGM_FI_DEV_ECC_DBE_AGG_TOTAL",  # 双位聚合（持久）ECC 错误总数 (Total double bit aggregate (persistent) ECC errors) 注：单调递增。
+    "DCGM_FI_DEV_ECC_DBE_VOL_TOTAL",  # 双位易失性 ECC 错误总数。(Total double bit volatile ECC errors)
+    "DCGM_FI_DEV_ECC_SBE_AGG_TOTAL",  # 单位聚合（持久）ECC 错误总数 注：单调递增。
+    "DCGM_FI_DEV_ECC_SBE_VOL_TOTAL",  # 单位易失性 ECC 错误总数。
+    "DCGM_FI_DEV_ENC_UTIL",  # GPU 编码器利用率（单位：%）
+    "DCGM_FI_DEV_FB_FREE",  # GPU 帧缓存剩余量（单位：MiB）
+    "DCGM_FI_DEV_FB_USED",  # GPU 帧缓存使用量（单位：MiB） (该值与nvidia-smi命令中memory-usage的已使用值对应)
+    "DCGM_FI_DEV_GPU_TEMP",  # GPU 当前温度（单位：℃）
+    "DCGM_FI_DEV_GPU_UTIL",  # GPU 利用率（单位：%）
+    "DCGM_FI_DEV_MEMORY_TEMP",  # 显存当前温度（单位：℃）
+    "DCGM_FI_DEV_MEM_CLOCK",  # GPU 内存时钟（单位：MHZ）
+    "DCGM_FI_DEV_MEM_COPY_UTIL",  # GPU 内存带宽利用率（单位：%）
+    "DCGM_FI_DEV_NVLINK_BANDWIDTH_TOTAL",  # GPU 所有通道的 NVLink 带宽计数器总数
+    "DCGM_FI_DEV_PCIE_REPLAY_COUNTER",  # GPU PCIE 重试次数
+    "DCGM_FI_DEV_POWER_USAGE",  # GPU 当前使用功率（单位：W)
+    "DCGM_FI_DEV_ROW_REMAP_FAILURE",  # Whether remapping of rows has failed
+    "DCGM_FI_DEV_SM_CLOCK",  # GPU SM 时钟（单位：MHZ）
+    "DCGM_FI_DEV_TOTAL_ENERGY_CONSUMPTION",  # GPU 启动以来的总能耗（单位：mJ）
+    "DCGM_FI_DEV_UNCORRECTABLE_REMAPPED_ROWS",  # Number of remapped rows for uncorrectable errors.
+    "DCGM_FI_DEV_VGPU_LICENSE_STATUS",  # vGPU 许可证状态
+    "DCGM_FI_DEV_XID_ERRORS",  # XID errors
+    "DCGM_FI_PROF_DRAM_ACTIVE",  # 内存接口主动发送或接收数据的周期比率 (The ratio of cycles the device memory interface is active sending or receiving data)
+    "DCGM_FI_PROF_GR_ENGINE_ACTIVE",  # Profiling Fields. These all start with DCGM_FI_PROF_* Ratio of time the graphics engine is active. The graphics engine is active if a graphics/compute context is bound and the graphics pipe or compute pipe is busy.
+    "DCGM_FI_PROF_PCIE_RX_BYTES",  # GPU PCIE 接收字节总数（单位：字节）
+    "DCGM_FI_PROF_PCIE_TX_BYTES",  # GPU PCIE 发送字节总数（单位：字节）
+    "DCGM_FI_PROF_PIPE_TENSOR_ACTIVE",  # The ratio of cycles the any tensor pipe is active (off the peak sustained elapsed cycles)
+    "DCGM_FI_PROF_SM_ACTIVE",  # The ratio of cycles an SM has at least 1 warp assigned (computed from the number of cycles and elapsed cycles)
+    "DCGM_FI_PROF_SM_OCCUPANCY",  # The ratio of number of warps resident on an SM.
+]
+
+table_dcgm_fatal_header = [
+    "DCGM_FI_DEV_XID_ERRORS",
+    "DCGM_FI_DEV_GPU_TEMP",
+    "DCGM_FI_DEV_MEMORY_TEMP",
+    "DCGM_FI_DEV_MEM_CLOCK",
+    "DCGM_FI_DEV_MEM_COPY_UTIL",
+    "DCGM_FI_DEV_FB_FREE",
+    "DCGM_FI_DEV_FB_USED",
+    "DCGM_FI_PROF_DRAM_ACTIVE",
+    "DCGM_FI_DEV_POWER_USAGE",
+    "DCGM_FI_DEV_GPU_UTIL",
+    "DCGM_FI_PROF_PIPE_TENSOR_ACTIVE",
+    "DCGM_FI_PROF_SM_ACTIVE",
+    "DCGM_FI_PROF_SM_OCCUPANCY",
+    "DCGM_FI_DEV_SM_CLOCK",
+]
+
+converted_dcgm_fatal_table_header = [
+    "XID_ERRORS",
+    "GPU_TEMP",
+    "MEMORY_TEMP",
+    "MEM_CLOCK",
+    "MEM_COPY_UTIL",
+    "FB_FREE",
+    "FB_USED",
+    "DRAM_ACTIVE",
+    "POWER_USAGE",
+    "GPU_UTIL",
+    "PIPE_TENSOR_ACTIVE",
+    "SM_ACTIVE",
+    "SM_OCCUPANCY",
+    "SM_CLOCK",
+]
+
+# node_infiniband_physical_state_id:
+# (0: no change, 1: sleep, 2: polling, 3: disable, 4: shift, 5: link up, 6: link error recover, 7: phytest)
+IB_PHYSICAL_STATE_MAP = {
+    "0": "no change",
+    "1": "sleep",
+    "2": "polling",
+    "3": "disable",
+    "4": "shift",
+    "5": "link up",
+    "6": "link error recover",
+    "7": "phytest",
+}
+
+
+# node_infiniband_state_id: (0: no change, 1: down, 2: init, 3: armed, 4: active, 5: act defer)
+IB_STATE_MAP = {"0": "no change", "1": "down", "2": "init", "3": "armed", "4": "active", "5": "act defer"}