Abstract Representation of Shared Data for Heterogeneous Computing

Kumar, T. K. Satish; Natarajan, Aravind; Ruan, Wenjia; Badr, Mario; Gracia, Darío Suárez; Caşcaval, Călin

doi:10.1007/978-3-030-35225-7_11

Cited by 1 publication

(1 citation statement)

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“…Kumar et al [25] provides a solution (a middle layer) to programming difficulties of data management posed by the many platform-specific intricacies on heterogeneous platforms. Ramashekar et al [40], Wang et al [51] and Pichai et al [38] study memory management and data transfer on CPU-GPU system.…”

Section: Related Workmentioning

confidence: 99%

HiWayLib

Zheng

Zhai

et al. 2019

Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Syst

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Pipeline is a parallel computing model underpinning a class of important applications running on CPU-GPU heterogeneous systems. A critical aspect for the efficiency of such applications is the support of communications among pipeline stages that may reside on CPU and different parts of a GPU. Existing libraries of concurrent data structures do not meet the needs, due to the massive parallelism on GPU and the complexities in CPU-GPU memory and connections. This work gives an in-depth study on the communication problem. It identifies three key issues, namely, slow and errorprone detection of the end of pipeline processing, intensive queue contentions on GPU, and cumbersome inter-device data movements. This work offers solutions to each of the issues, and integrates all together to form a unified library named HiWayLib. Experiments show that HiWayLib significantly boosts the efficiency of pipeline communications in CPU-GPU heterogeneous applications. For real-world applications, HiWayLib produces 1.22~2.13× speedups over the state-of-art implementations with little extra programming effort required. CCS Concepts • General and reference → Performance; • Computing methodologies → Parallel computing methodologies; • Computer systems organization → Heterogeneous (hybrid) systems.

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Section: Related Workmentioning

confidence: 99%