Most of my research happens in improving computer system infrastructure for deep learning applications. This ranges from GPU sharing primitives to facilitate better scheduling and improve resource utilization, to better hyperparameter tuning execution engine providing better utilization in the cluster. I recently joined Google working on GPU and accelerators in the cluster.
For a complete list of my experience, please refer to my CV (last updated: Sep. 2021).
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IEEE Micro Volume 41, Issue 5 (IEEE Micro 41(5))
In this article, we present a system to collectively optimize efficiency in a very large scale deployment of GPU servers for machine learning workloads at Facebook. Our system 1) measures and stores system-wide efficiency metrics for every executed workflow; 2) aggregates data from across the execution stack to identify optimization opportunities that maximize fleet-wide efficiency improvements; 3) provides periodic and on-demand whole-system profiling for workflows; and 4) automatically analyzes traces for common antipatterns. We present each component of the stack and show case studies demonstrating the use of the tools to significantly improve performance. To our knowledge, our system is the most complete and effective solution for identifying and addressing efficiency problems in datacenter-scale GPU deployments.
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The 4th Conference on Machine Learning and Systems (MLSys'21) (Acceptance Rate: 23.5%)
Current hyperparameter tuning solutions lack complementary execution engines to efficiently leverage distributed computation, thus ignoring the possibility of intra- and inter-GPU sharing, which exhibits poor resource usage. In this paper, we present Fluid, a generalized hyperparameter tuning execution engine, that coordinates between hyperparameter tuning jobs and cluster resources. Fluid schedules evaluation trials in such jobs using a waterfilling approach to make the best use of resources both at intra- and inter-GPU granularities to speed up the tuning process. By abstracting a hyperparameter tuning job as a sequence of TrialGroup, Fluid can boost the performance of diverse hyperparameter tuning solutions. Our experiments show that Fluid can speed up synchronous BOHB by 100%, and BOHB and ASHA by 30% while having similar final accuracy.
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Artifacts Available Artifacts Evaluated Functional Results ReplicatedThe 3rd Conference on Machine Learning and Systems (MLSys'20) (Acceptance Rate: 19.2%)
Unlike traditional resources such as CPU or the network, modern GPUs do not natively support fine-grained sharing primitives. Consequently, implementing common policies such as time sharing and preemption are expensive. Worse, when a deep learning (DL) application cannot completely use a GPU’s resources, the GPU cannot be efficiently shared between multiple applications, leading to GPU underutilization.
We present Salus to enable two GPU sharing primitives: fast job switching and memory sharing, to achieve fine-grained GPU sharing among multiple DL applications. Salus is an efficient, consolidated execution service that exposes the GPU to different DL applications, and it enforces fine-grained sharing by performing iteration scheduling and addressing associated memory management issues. We show that these primitives can then be used to implement flexible sharing policies. Our integration of Salus with TensorFlow and evaluation on popular DL jobs shows that Salus can improve the average completion time of DL training jobs by
, GPU utilization for hyper-parameter tuning by , and GPU utilization of DL inference applications by over not sharing the GPU and over NVIDIA MPS with small overhead.
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arXiv (arXiv:1902.04610)
GPU computing is becoming increasingly more popular with the proliferation of deep learning (DL) applications. However, unlike traditional resources such as CPU or the network, modern GPUs do not natively support fine-grained sharing primitives. Consequently, implementing common policies such as time sharing and preemption are expensive. Worse, when a DL application cannot completely use a GPU’s resources, the GPU cannot be efficiently shared between multiple applications, leading to GPU underutilization.
We present Salus to enable two GPU sharing primitives: fast job switching and memory sharing, in order to achieve fine-grained GPU sharing among multiple DL applications. Salus implements an efficient, consolidated execution service that exposes the GPU to different DL applications, and enforces fine-grained sharing by performing iteration scheduling and addressing associated memory management issues. We show that these primitives can then be used to implement flexible sharing policies such as fairness, prioritization, and packing for various use cases. Our integration of Salus with TensorFlow and evaluation on popular DL jobs show that Salus can improve the average completion time of DL training jobs by
, GPU utilization for hyper-parameter tuning by , and GPU utilization of DL inference applications by over not sharing the GPU and over NVIDIA MPS with small overhead.
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AI for Seciety - A Michigan AI Symposium (MichiganAI'18)
GPU computing is becoming increasingly more popular with the proliferation of deep learning (DL) applications. However, unlike traditional resources such as CPU or the network, modern GPUs do not natively support fine-grained sharing primitives. Consequently, implementing common policies such as time sharing and preemption are expensive. Worse, when a DL application cannot completely use a GPU’s resources, the GPU cannot be efficiently shared between multiple applications, leading to GPU underutilization.
We present Salus to enable two GPU sharing primitives: fast job switching and memory sharing, in order to achieve fine-grained GPU sharing among multiple DL applications. Salus implements an efficient, consolidated execution service that exposes the GPU to different DL applications, and enforces fine-grained sharing by performing iteration scheduling and addressing associated memory management issues. We show that these primitives can then be used to implement flexible sharing policies such as fairness, prioritization, and packing for various use cases. Our integration of Salus with TensorFlow and evaluation on popular DL jobs show that Salus can improve the average completion time of DL training jobs by 3.19×, GPU utilization for hyper-parameter tuning by 2.38×, and GPU utilization of DL inference applications by 42× over not sharing the GPU and 7× over NVIDIA MPS with small overhead.
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SysML Conference 2018 (SysML'18)
In this paper, we present Salus, a framework-independent runtime to enable fine-grained sharing of a single GPU among multiple memory-intensive CNN applications. Salus implements an efficient, consolidated execution service that exposes the GPU to different CNN applications and enforces fine-grained sharing by performing low-level memory management, managing GPU task scheduling, and addressing associated issues such as deadlock prevention and GPU-to-host memory paging. Not only can Salus enable multiple CNN jobs to share a single GPU, it can enforce sharing policies to provide fairness and prioritization as well. Our integration of Salus with TensorFlow shows that it can improve GPU utilization by up to 20x.
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The 16th Workshop on Hot Topics in Operating Systems (HotOS'17)
In recent years, deep learning has pervaded many areas of computing due to the confluence of an explosive growth of large-scale computing capabilities, availability of datasets, and advances in learning techniques. While this rapid growth has resulted in diverse deep learning frameworks, it has also led to inefficiencies for both the users and developers of these frameworks. Specifically, adopting useful techniques across frameworks – both to perform learning tasks and to optimize performance – involves significant repetitions and reinventions.
In this paper, we observe that despite their diverse origins, many of these frameworks share architectural similarities. We argue that by introducing a common representation of learning tasks and a hardware abstraction model to capture compute heterogeneity, we might be able to relieve machine learning researchers from dealing with low-level systems issues and systems researchers from being tied to any specific framework. We expect this decoupling to accelerate progress in both domains.