A scalable processing-in-memory accelerator for parallel graph processing
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Cites background or methods from "A scalable processing-in-memory acc..."
...Overall GaaS-X achieves 7.7x speedup and 22x energy savings over GraphR which in turn shows up to 4x performance and 4x-10x energy efficiency gains over Tesseract [1]....
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...Improving on Tesseract, GraphQ [47], proposes a hybrid execution model to maximize intra and inter-array throughput....
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...7x speedup and 22x energy savings over GraphR which in turn shows up to 4x performance and 4x-10x energy efficiency gains over Tesseract [1]....
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...These can be broadly categorized into conventional von Neumann architecture-based digital accelerators [13], [28] and processing-in-memory (PIM) accelerators [1], [20], [33], [44]....
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...In contrast to the existing DRAM-based PIM accelerators (Tesseract, GraphIA and GraphQ), GaaS-X incorporates insitu sequential processing of sparse graph data on the ReRAM crossbar memories....
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"A scalable processing-in-memory acc..." refers methods in this paper
...Our comprehensive evaluations using five state-of-the-art graph processing workloads with large real-world graphs show that the proposed architecture improves average system performance by a factor of ten and achieves 87% average energy reduction over conventional systems....
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"A scalable processing-in-memory acc..." refers methods in this paper
...For this purpose, we use METIS [27] to perform 512-way multi-constraint partitioning to balance the number of vertices, outgoing edges, and incoming edges of each partition, as done in a recent previous work [51]....
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...This is confirmed by the observation that Tesseract with METIS spends 59% of execution time waiting for synchronization barriers....
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4,019 citations
"A scalable processing-in-memory acc..." refers methods in this paper
...We evaluate our architecture using an in-house cycle-accurate x86-64 simulator whose frontend is Pin [38]....
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3,840 citations
"A scalable processing-in-memory acc..." refers methods in this paper
...Our comprehensive evaluations using five state-of-the-art graph processing workloads with large real-world graphs show that the proposed architecture improves average system performance by a factor of ten and achieves 87% average energy reduction over conventional systems....
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...It also includes two hardware prefetchers specialized for memory access patterns of graph processing, which operate based on the hints provided by our programming model....
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