DAMOV: A New Methodology and Benchmark Suite for Evaluating Data Movement Bottlenecks

Geraldo F. Oliveira; Juan Gómez-Luna; Lois Orosa; Saugata Ghose; Nandita Vijaykumar; Ivan Fernandez; Mohammad Sadrosadati; Onur Mutlu

arXiv · 2105.03725

DAMOV: A New Methodology and Benchmark Suite for Evaluating Data Movement Bottlenecks

Geraldo F. Oliveira·Juan Gómez-Luna·Lois Orosa·Saugata Ghose·Nandita Vijaykumar·Ivan Fernandez·Mohammad Sadrosadati·Onur Mutlu

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Abstract

Data movement between the CPU and main memory is a first-order obstacle against improving performance, scalability, and energy efficiency in modern systems. Computer systems employ a range of techniques to reduce overheads tied to data movement, spanning from traditional mechanisms (e.g., deep multi-level cache hierarchies, aggressive hardware prefetchers) to emerging techniques such as Near-Data Processing (NDP), where some computation is moved close to memory. Our goal is to methodically identify potential sources of data movement over a broad set of applications and to comprehensively compare traditional compute-centric data movement mitigation techniques to more memory-centric techniques, thereby developing a rigorous understanding of the best techniques to mitigate each source of data movement. With this goal in mind, we perform the first large-scale characterization of a wide variety of applications, across a wide range of application domains, to identify fundamental program properties that lead to data movement to/from main memory. We develop the first systematic methodology to classify applications based on the sources contributing to data movement bottlenecks. From our large-scale characterization of 77K functions across 345 applications, we select 144 functions to form the first open-source benchmark suite (DAMOV) for main memory data movement studies. We select a diverse range of functions that (1) represent different types of data movement bottlenecks, and (2) come from a wide range of application domains. Using NDP as a case study, we identify new insights about the different data movement bottlenecks and use these insights to determine the most suitable data movement mitigation mechanism for a particular application. We open-source DAMOV and the complete source code for our new characterization methodology at https://github.com/CMU-SAFARI/DAMOV.

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Geraldo F. Oliveira, Juan Gómez-Luna, Lois Orosa, Saugata Ghose, Nandita Vijaykumar, Ivan Fernandez, Mohammad Sadrosadati, Onur Mutlu. 2023-04-06. DAMOV: A New Methodology and Benchmark Suite for Evaluating Data Movement Bottlenecks. https://arxiv.org/abs/2105.03725

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