Evaluating Machine Learning Workloads on Memory-Centric Computing Systems
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Date
2023
Publication Type
Conference Paper
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yes
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Abstract
Training machine learning (ML) algorithms is a computationally intensive process, which is frequently memory-bound due to repeatedly accessing large training datasets. As a result, processor-centric systems (CPU, GPU) waste large amounts of energy and execution cycles due to the data movement between memory units and processing units. Memory-centric computing systems, i.e., systems with processing-in-memory (PIM) capabilities, can alleviate this data movement bottleneck. Our goal is to understand the potential of general-purpose PIM architectures to accelerate ML training. To do so, we (1) implement several classic ML algorithms (namely, linear regression, logistic regression, decision tree, K-Means clustering) on a real-world generalpurpose PIM architecture, (2) evaluate and characterize them in terms of accuracy, performance and scaling, and (3) compare to their counterpart state-of-the-art implementations on CPU and GPU. Our evaluation on a real memory-centric computing system with more than 2500 PIM cores shows that PIM greatly accelerates memorybound ML workloads, when the necessary operations and datatypes are natively supported by PIM hardware. For example, our PIM implementation of decision tree is 27× faster than the CPU implementation on an 8-core Intel Xeon, and 1.34× faster than the GPU implementation on an NVIDIA A100. Our PIM implementation of K-Means clustering is 2.8× and 3.2× faster than CPU and GPU implementations, respectively. We provide several key observations, takeaways, and recommendations for users of ML workloads, programmers of PIM architectures, and hardware designers and architects of future memory-centric computing systems. We open-source all our code and datasets at https://github.com/CMU-SA FARI/pim-ml.
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published
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Book title
2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)
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Pages / Article No.
35 - 49
Publisher
IEEE
Event
2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS 2023)
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Methods
Software
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Subject
machine learning; processing-in-memory; regression; classification; clustering; benchmarking; memory bottleneck
Organisational unit
09483 - Mutlu, Onur / Mutlu, Onur