Abstract
Data movement between the CPU and main memory is a first-order obstacle against improv ing 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 hierarch ies, aggressive hardware prefetcher s) to emerging techniques such as Near-Data Processing (NDP), where some computation is moved close to memory. Prior NDP works investigate the root causes of data movement bottlenecks using different profiling methodologies and tools. However, there is still a lack of understanding about the key metrics that can identify different data movement bottlenecks and their relation to traditional and emerging data movement mitigation mechanisms. 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 (e.g., cach ing and prefetch ing) to more memory-centric techniques (e.g., NDP), 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 .
Highlights
T ODAY’S computing systems require moving data from main memory to the CPU cores so that computation can take place on the data
In Near-Data Processing (NDP), the computational logic close to memory has access to data that resides in main memory with significantly higher memory bandwidth, lower latency, and lower energy consumption than the CPU has in existing systems
We find a set of 144 functions that take at least 3% of the total clock cycles and have a value of the Memory Bound metric greater or equal to 30%, which forms the basis of DAMOV, our new data movement benchmark suite
Summary
T ODAY’S computing systems require moving data from main memory (consisting of DRAM) to the CPU cores so that computation can take place on the data This data movement is a major bottleneck for system performance and energy consumption [1, 2]. Oliveira et al.: DAMOV: A New Methodology and Benchmark Suite for Evaluating Data Movement Bottlenecks cache hierarchies and aggressive prefetchers Such mechanisms come with significant hardware cost and complexity, but they often fail to hide the latency and energy costs of accessing DRAM in many modern and emerging applications [1, 5, 50]–[52]. This happens since the external memory bandwidth is bounded by the limited number of I/O pins available in the DRAM device [121]
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