DaPPA: A Data-Parallel Programming Framework for Processing-in-Memory Architectures
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866909586864209920 |
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| author | Oliveira, Geraldo F. Kohli, Alain Novo, David Olgun, Ataberk Yaglikci, A. Giray Ghose, Saugata Gómez-Luna, Juan Mutlu, Onur |
| author_facet | Oliveira, Geraldo F. Kohli, Alain Novo, David Olgun, Ataberk Yaglikci, A. Giray Ghose, Saugata Gómez-Luna, Juan Mutlu, Onur |
| contents | The growing volume of data in modern applications has led to significant computational costs in conventional processor-centric systems. Processing-in-memory (PIM) architectures alleviate these costs by moving computation closer to memory, reducing data movement overheads. UPMEM is the first commercially available PIM system, featuring thousands of in-order processors (DPUs) integrated within DRAM modules. However, a programming UPMEM-based system remains challenging due to the need for explicit data management and workload partitioning across DPUs.
We introduce DaPPA (data-parallel processing-in-memory architecture), a programming framework that eases the programmability of UPMEM systems by automatically managing data movement, memory allocation, and workload distribution. The key idea behind DaPPA is to leverage a high-level data-parallel pattern-based programming interface to abstract hardware complexities away from the programmer. DaPPA comprises three main components: (i) data-parallel pattern APIs, a collection of five primary data-parallel pattern primitives that allow the programmer to express data transformations within an application; (ii) a dataflow programming interface, which allows the programmer to define how data moves across data-parallel patterns; and (iii) a dynamic template-based compilation, which leverages code skeletons and dynamic code transformations to convert data-parallel patterns implemented via the dataflow programming interface into an optimized UPMEM binary.
We evaluate DaPPA using six workloads from the PrIM benchmark suite on a real UPMEM system. Compared to hand-tuned implementations, DaPPA improves end-to-end performance by 2.1x, on average, and reduces programming complexity (measured in lines-of-code) by 94%. Our results demonstrate that DaPPA is an effective programming framework for efficient and user-friendly programming on UPMEM systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2310_10168 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DaPPA: A Data-Parallel Programming Framework for Processing-in-Memory Architectures Oliveira, Geraldo F. Kohli, Alain Novo, David Olgun, Ataberk Yaglikci, A. Giray Ghose, Saugata Gómez-Luna, Juan Mutlu, Onur Hardware Architecture The growing volume of data in modern applications has led to significant computational costs in conventional processor-centric systems. Processing-in-memory (PIM) architectures alleviate these costs by moving computation closer to memory, reducing data movement overheads. UPMEM is the first commercially available PIM system, featuring thousands of in-order processors (DPUs) integrated within DRAM modules. However, a programming UPMEM-based system remains challenging due to the need for explicit data management and workload partitioning across DPUs. We introduce DaPPA (data-parallel processing-in-memory architecture), a programming framework that eases the programmability of UPMEM systems by automatically managing data movement, memory allocation, and workload distribution. The key idea behind DaPPA is to leverage a high-level data-parallel pattern-based programming interface to abstract hardware complexities away from the programmer. DaPPA comprises three main components: (i) data-parallel pattern APIs, a collection of five primary data-parallel pattern primitives that allow the programmer to express data transformations within an application; (ii) a dataflow programming interface, which allows the programmer to define how data moves across data-parallel patterns; and (iii) a dynamic template-based compilation, which leverages code skeletons and dynamic code transformations to convert data-parallel patterns implemented via the dataflow programming interface into an optimized UPMEM binary. We evaluate DaPPA using six workloads from the PrIM benchmark suite on a real UPMEM system. Compared to hand-tuned implementations, DaPPA improves end-to-end performance by 2.1x, on average, and reduces programming complexity (measured in lines-of-code) by 94%. Our results demonstrate that DaPPA is an effective programming framework for efficient and user-friendly programming on UPMEM systems. |
| title | DaPPA: A Data-Parallel Programming Framework for Processing-in-Memory Architectures |
| topic | Hardware Architecture |
| url | https://arxiv.org/abs/2310.10168 |