Add Perseus news and write Perseus blog (#107)

README.md

@@ -15,10 +15,10 @@
 ---
 **Project News** ⚡ 
 
+- \[2024/08\] Perseus, an optimizer for large model training, was accepted to SOSP'24! [Preprint](https://arxiv.org/abs/2312.06902) | [Blog](https://ml.energy/zeus/research_overview/perseus) | [Optimizer](https://ml.energy/zeus/optimize/pipeline_frequency_optimizer)
 - \[2024/07\] Added AMD GPU, CPU, and DRAM energy measurement support, and preliminary JAX support!
 - \[2024/05\] Zeus is now a PyTorch ecosystem project. Read the PyTorch blog post [here](https://pytorch.org/blog/zeus/)!
 - \[2024/02\] Zeus was selected as a [2024 Mozilla Technology Fund awardee](https://foundation.mozilla.org/en/blog/open-source-AI-for-environmental-justice/)!
-- \[2023/12\] We released Perseus, an energy optimizer for large model training: [Preprint](https://arxiv.org/abs/2312.06902) | [Blog](https://ml.energy/zeus/research_overview/perseus) | [Optimizer](https://ml.energy/zeus/optimize/pipeline_frequency_optimizer)
 - \[2023/07\] We used the [`ZeusMonitor`](https://ml.energy/zeus/reference/monitor/energy/#zeus.monitor.energy.ZeusMonitor) to profile GPU time and energy consumption for the [ML.ENERGY leaderboard & Colosseum](https://ml.energy/leaderboard).
 ---
 

docs/index.md

@@ -14,10 +14,10 @@ hide:
 ---
 **Project News** ⚡ 
 
+- \[2024/08\] Perseus, our optimizer for large model training, was accepted to SOSP'24! [Preprint](https://arxiv.org/abs/2312.06902){.external} | [Blog](research_overview/perseus.md) | [Optimizer](optimize/pipeline_frequency_optimizer.md)
 - \[2024/07\] Added AMD GPU, CPU, and DRAM energy measurement support, and preliminary JAX support!
 - \[2024/05\] Zeus is now a PyTorch ecosystem project. Read the PyTorch blog post [here](https://pytorch.org/blog/zeus/){.external}!
 - \[2024/02\] Zeus was selected as a [2024 Mozilla Technology Fund awardee](https://foundation.mozilla.org/en/blog/open-source-AI-for-environmental-justice/){.external}!
-- \[2023/12\] We released Perseus, an energy optimizer for large model training: [Preprint](https://arxiv.org/abs/2312.06902){.external} | [Blog](research_overview/perseus.md) | [Optimizer](optimize/pipeline_frequency_optimizer.md)
 - \[2023/07\] We used the [`ZeusMonitor`][zeus.monitor.ZeusMonitor] to profile GPU time and energy consumption for the [ML.ENERGY leaderboard & Colosseum](https://ml.energy/leaderboard){.external}.
 ---
 

docs/research_overview/index.md

@@ -3,7 +3,7 @@
 Zeus is rooted on multiple research papers.
 Even more research is ongoing, and Zeus will continue to expand and get better at what it's doing.
 
-1. Zeus (2023): [Paper](https://www.usenix.org/conference/nsdi23/presentation/you) | [Blog](zeus.md) | [Slides](https://www.usenix.org/system/files/nsdi23_slides_chung.pdf)
-1. Chase (2023): [Paper](https://arxiv.org/abs/2303.02508)
-1. Perseus (2023): [Paper](https://arxiv.org/abs/2312.06902) | [Blog](perseus.md)
+1. Zeus (NSDI, 2023): [Paper](https://www.usenix.org/conference/nsdi23/presentation/you) | [Blog](zeus.md) | [Slides](https://www.usenix.org/system/files/nsdi23_slides_chung.pdf)
+1. Chase (ICLRW, 2023): [Paper](https://arxiv.org/abs/2303.02508)
+1. Perseus (SOSP, 2024): [Preprint](https://arxiv.org/abs/2312.06902) | [Blog](perseus.md)
 

docs/research_overview/perseus.md

@@ -1,27 +1,89 @@
 <div align="center" markdown>
-<h1>Perseus: Removing Energy Bloat<br>from Large Model Training</h1>
+<h1>Perseus:<br>Reducing Energy Bloat in Large Model Training</h1>
+
+SOSP '24 (To appear)
+
 [**Preprint**](https://arxiv.org/abs/2312.06902)
 </div>
 
-Perseus finds GPU frequency plans on the training time--energy Pareto frontier of large model training.
-These plans include one that does not make training any slower compared to not using Perseus, but yields free energy savings.
-If you have a bit more leeway as to when training should finish (e.g., You're good as long as training finishes by tomorrow morning), you can pick the frequency plan that slows down training by a couple percentages and save more energy.
+<div class="critic-dark" markdown>
+{==
+
+## Abstract
+
+With the rise of GenAI, power delivery became one of the largest limiting factors in building and operating datacenters for AI workloads.
+However, we observe that not all energy consumed during training directly contributes to end-to-end throughput, and a significant portion can be removed without slowing down training, which we call energy bloat.
+
+In this work, we identify two independent sources of energy bloat in large model training and propose Perseus, a training system that mitigates both.
+To do this, Perseus obtains the “iteration time–energy” Pareto frontier of any large model training job using an efficient graph cut-based algorithm and schedules the energy consumption of computations across time to reduce both types of energy bloat.
+Evaluation on large models like GPT-3 shows that Perseus reduces the energy consumption of large model training by up to 30% with little throughput loss and no hardware modification.
+
+==}
+</div>
+
+## The Energy Bottleneck
+
+> *"We would probably build out bigger clusters if we could get the energy to do it."*
+
+> *"No one has built a 1 GW datacenter yet. I think it will happen. This is only a matter of time."*
+
+-- [Mark Zuckerberg's interview with Dwarkesh Patel](https://youtu.be/bc6uFV9CJGg?si=xPbEFkHpNP8T_FOb&t=1679)
+
+Exponentially growing things only do so until they hit a bottleneck, which becomes the next big challenge to solve.
+Today, energy is one of such bottlenecks for GenAI.
+People need more compute (usually from GPUs) to train and serve large models, but it's very difficult to get access to electricity which ultimately powers those hardware.[^1][^2]
+
+The goal of perseus is to reduce the total energy consumption of large model training without slowing down training by finding and removing energy wastage during training, which we call *energy bloat*.
+This leads to both less total energy consumption and lower average power draw.
+
+## Energy Bloat
+
+The core idea of energy bloat is that if some computation is running at an *unnecessarily fast* speed, it may be wasting energy.
+Perseus identifies two independent sources of energy bloat for training pipelines (pipeline as in pipeline parallel training), and proposes an optimization method that reduces both.
+
+### Intrinsic Bloat
+
+Large model training requires the distribution of work across multiple GPUs using a combination of multiple parallelization methods.
+The core observation of Perseus is that especially for pipeline parallelism, work cannot be perfectly split and balanced across every GPU; some GPUs have more work to do and some less.
+GPUs with smaller amounts of work finish before GPUs with more amounts of work, but ultimately training throughput is bound by GPUs with the most amount of work.
+In other words, GPUs with lighter load are running unnecessarily fast and generating energy bloat.
+
+### Extrinsic Bloat
+
+In large scale training that involves tens of thousands of GPUs, *stragglers* (or slowdowns) become a reality -- hardware and software faults, slowdowns due to thermal and power throttling, data pipeline stalls, and more.
+When GPU stragglers emerge, the training pipeline (among multiple data parallel pipelines) that contains the straggler GPU will slow down, and *every other* pipeline must wait for the straggler pipeline to finish before they can synchronize gradients and move on to the next iteration.
+This means that when a straggler pipeline emerges, running other pipelines at their full speed is wasting energy -- they can slow down and reduce energy bloat.
+
+## Reducing Energy Bloat
+
+To reduce intrinsic bloat, we need to precisely slow down select computations in the pipeline without affecting its end-to-end iteration time.
+On the other hand, to reduce extrinsic bloat, we need to figure out how to make the whole pipeline slower, while *keeping* intrinsic bloat low.
 
 <figure>
 <img src="../img/iteration_time_energy_frontier.svg" width=400px>
 <figcaption>Perseus discovers the entire iteration time--energy Pareto frontier.</figcaption>
 </figure>
 
-How is this done?
-Large model training requires the distribution of work across multiple GPUs using a combination of multiple parallelization methods.
-The core observation of Perseus is that especially for pipeline parallelism, work cannot be perfectly split and balanced across every GPU; some GPUs have more work to do and some less.
-GPUs with smaller amounts of work finish before GPUs with more amounts of work, but ultimately training throughput is bound by GPUs with the most amount of work.
-In other words, GPUs with lighter load are running unnecessarily fast and wasting energy (i.e., there is **energy bloat**).
+To do so, Perseus pre-characterizes every GPU frequency plan on the Iteration time--energy Pareto frontier upfront.
+Every frequency plan on this Pareto frontier has low intrinsic bloat, and when a straggler pipeline emerges, Perseus can simply look up the frequency plan that leads to the right pipeline iteration time on the frontier and deploy it to all non-straggler pipelines.
+
+You can also see Perseus's optimizer in action:
 
 <figure>
-  <img src="../../research_overview/img/wide-resnet.gif" width=600px>
-  <figcaption>The pipeline frequency optimizer in action</figcaption>
+  <img src="../img/wide-resnet.gif" width=600px>
+  <figcaption>Perseus optimizer in action</figcaption>
 </figure>
 
-We reduce energy bloat by controlling the execution speed of each pipeline instruction (forward and backward) in each stage by controlling the GPU's frequency in a fine-grained manner.
-We call the assignment of a GPU frequency to each pipeline instruction *frequency plan*, and Perseus gives you **every Pareto-optimal frequency plan** that you can choose any point on the iteration time--energy Pareto frontier.
+As you can see, Perseus controls the GPU frequency of each forward and backward computation in one training pipeline.
+One training pipeline is actually a DAG of computations.
+Assigning the right GPU frequency to each computation while controlling the end-to-end execution time of the DAG and minimizing its total energy consumption happens to be NP-Hard problem, but Perseus introduces a cool graph cut-based algorithm that produces high-quality approximate solutions.
+Check out the algorithm in our paper!
+
+## Using Perseus
+
+Perseus is open-sourced as the [**Pipeline Frequency Optimizer**](../optimize/pipeline_frequency_optimizer.md).
+It's still in early-stage development and we have a lot of sharp edges to cut, but we're hoping to talk more with the community to drive its development.
+Let's chat!
+
+[^1]: CBRE, Global Data Center Trends 2023, [https://www.cbre.com/insights/reports/global-data-center-trends-2023](https://www.cbre.com/insights/reports/global-data-center-trends-2023)
+[^2]: CBRE, Global Data Center Trends 2024, [https://www.cbre.com/insights/reports/global-data-center-trends-2024](https://www.cbre.com/insights/reports/global-data-center-trends-2024)

docs/research_overview/zeus.md

@@ -1,6 +1,8 @@
 <div align="center" markdown>
 <h1>Zeus: Understanding and Optimizing<br>GPU Energy Consumption of DNN Training</h1>
 
+NSDI '23
+
 [**Paper**](https://www.usenix.org/conference/nsdi23/presentation/you) | [**Slides**](https://www.usenix.org/system/files/nsdi23_slides_chung.pdf) | [**YouTube**](https://youtu.be/aZoD-jgO3fE)
 </div>