Collaborative Research: OAC Core: CropDL - Scheduling and Checkpoint/Restart Support for Deep Learning Applications on HPC Clusters

机器学习（ML）和深度学习（DL）（更具体地说，深度神经网络（DNN））工作负载开始主导高性能计算（HPC）竞技场。如今，即使是训练一个最先进的深度学习模型（例如，大型语言模型或LLM）。随着对大规模DNN模型培训的需求持续不断，并从私营部门扩展到NSF支持的科学家和工程师（他们更有可能使用共享计算资源），高效的检查点正在成为一个关键需求。检查点不仅有助于处理故障，还可以在共享HPC资源上提供更大的调度灵活性，因为一个非常长时间运行的作业可以分解为几个较短的作业。CropDL项目的前提是，高效和自动化的应用程序级检查点和重启对于促进使用共享HPC集群进行长时间运行的ML训练任务至关重要，从而大大增加了能够成功训练各种应用程序的大型ML模型的研究人员数量。该项目还在多个方面为教育和多样性做出了贡献，例如：1）引入课程（或课程材料），以引起人们对计算机系统本科和研究生教育中ML相关工作量的关注; 2）将该项目的研究任务与大学的协同研究计划相结合，以增加妇女和代表性不足的少数群体的参与;支持和培养博士研究生，为与新兴ML工作负载相关的系统和网络基础设施研究创造动力，并推广将这些工作负载的属性与现代HPC硬件的复杂性相结合的综合研究。CropDL的总体目标是支持应用级检查点/重新启动深度学习应用程序，以获得更好的弹性、更快的平均完成时间和更高的资源利用率。特别地，DL工作负载的若干属性（与科学计算相比）为检查点创建不同的机会和挑战：1）在并行执行期间有限的通信模式，这可以实现有效的协调检查点，2）用于压缩检查点的许多独特机会，并且可能采用不协调的检查点，以及3）可延展的执行，其中可以从不同数目的节点重新启动。基于这一观察，该项目的第一个方向是利用DNN模型的属性在检查点过程中进行训练。这包括各种并行模型下的DL应用程序的异步版本化检查点，以及基于内容的数据减少（压缩和稀疏化）技术，以减少检查点数量。研究的第二个方向集中在使用当前和即将到来的HPC系统的资源，同时检查点。它将DL应用程序的任务、数据和I/O需求制定为DAG表示，并开发了调度它们的方法。它还支持使用新兴I/O平台的深度学习应用程序的高效I/O。最后一个方向是通过基于DL工作负载的计算图的编译系统来自动化检查点。所有这些努力都考虑了DNN的各种并行化方案，即，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Machine Learning (ML) and Deep Learning (DL) (more specifically, Deep Neural Network (DNN)) workloads are beginning to dominate the High-Performance Computing (HPC) arena. Today, massive computational resources are required to train even a single state-of-the-art deep learning model (e.g., large language models or LLMs). As the need for training massive DNN models continues and expands from the private sector to NSF-supported scientists and engineers (who are more likely to use shared computing resources), efficient checkpointing is emerging as a critical need. Checkpointing not only helps deal with failures but also provides more scheduling flexibility on shared HPC resources, as a very long-running job can be broken into several shorter ones. The premise of the CropDL project is that efficient and automated application-level checkpoint and restart will be critical to facilitating the use of shared HPC clusters for long-running ML training tasks, drastically increasing the number of researchers that can successfully train large ML models for various applications. This project also contributes to education and diversity in multiple aspects, for example, 1) introducing courses (or course material) to bring attention to ML-related workloads in computer systems undergraduate and graduate education; 2) integrating research tasks from this project with synergistic research programs at universities to increase the participation of women and underrepresented minority groups; and 3) supporting and training PhD students in their research, creating momentum on systems and cyberinfrastructure research related to emerging ML workloads and popularizing integrative research that combines the properties of these workloads with the complexities of modern HPC hardware.The overarching goal of CropDL is to support application-level checkpoints/restarts of deep learning applications for better resiliency, faster average completion time, and higher resource utilization. Particularly, several properties of DL workloads (as compared to scientific computations) create distinct sets of opportunities and challenges for checkpointing: 1) limited communication patterns during parallel execution, which can enable efficient coordinated checkpoints, 2) many unique opportunities for compression of checkpoints, and possibly taking uncoordinated checkpoints, and 3) malleable execution, where restarting from a different number of nodes is possible. Based on this observation, the first direction of this project is to exploit the properties of the DNN model(s) to be trained during checkpointing. This includes asynchronous versioned checkpointing for DL applications under a wide variety of parallelism models as well as content-based data reduction (compression and sparsification) techniques to reduce checkpoint volumes. The second direction of research focuses on using current and upcoming HPC systems' resources efficiently while checkpointing. It formulates tasks, data, and I/O requirements from DL applications into DAG representations and develops methods to schedule them. It also supports efficient I/O for deep learning applications with emerging I/O platforms. The last direction is to automate checkpointing through a compilation system based on the computational graph of DL workloads. All these efforts consider a variety of parallelization schemes for DNNs, i.e., data, model, and/or pipelined parallelism.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.