GraphPIM: Enabling Instruction-Level PIM Offloading in Graph Computing Frameworks

By Lifeng Nai, Ramyad Hadidi, Jaewoong Sim, Hyojong Kim, Pranith Kumar, Hyesoon Kim. International Symposium on High-Performance Computer Architecture, 2017

302 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Processing Data Where It Makes Sense: Enabling In-Memory Computation (2019, background): “A popular modern application is large-scale graph processing [87, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193].”
Google Workloads for Consumer Devices: Mitigating Data Movement Bottlenecks (2018, unknown): “Recent works on PIM embed computation units within the logic layer (e.g., [3–5, 12, 30, 35, 36, 56, 59, 65, 66, 81, 82, 98, 99, 106, 116, 138, 150, 151, 153]).”
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing (2019, unknown): “We focus on DIMM-based near-memory processing [8], [23], [74] instead of resorting to specialized 2.5D/3D integration processes (e.g. HBM) [37], [39], [41].”
RowHammer: A Retrospective (2019, unknown): “, [13]–[15], [18], [22], [24], [34]–[36], [56], [63], [72], [77], [78], [80], [91], [92], [95], [96], [99], [101], [102], [111], [121]–[123], [151], [153], [154], [160], [161], [172], [178], [179], [182], [195], [198], [215]–[219], [221], [222], [228], [257], and [260]).”