HeTM: Transactional Memory for Heterogeneous Systems

Modern heterogeneous computing architectures, which couple multi-core CPUs with discrete many-core GPUs (or other specialized hardware accelerators), enable unprecedented peak performance and energy efficiency levels. However, developing applications that can take full advantage of the potential of...

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Vydané v:Proceedings / International Conference on Parallel Architectures and Compilation Techniques s. 232 - 244
Hlavní autori: Castro, Daniel, Romano, Paolo, Ilic, Aleksandar, Khan, Amin M.
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.09.2019
Predmet:
Computer architecture
computing
CPU
GPU
Graphics processing units
heterogeneous
memory
Performance evaluation
Programming
Synchronization
system
Task analysis
transaction
ISSN:2641-7936
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Popis
Shrnutí:Modern heterogeneous computing architectures, which couple multi-core CPUs with discrete many-core GPUs (or other specialized hardware accelerators), enable unprecedented peak performance and energy efficiency levels. However, developing applications that can take full advantage of the potential of heterogeneous systems is a notoriously hard task. This work takes a step towards reducing the complexity of programming heterogeneous systems by introducing the abstraction of Heterogeneous Transactional Memory (HeTM). HeTM provides programmers with the illusion of a single memory region, shared among the CPUs and the (discrete) GPU(s) of a heterogeneous system, with support for atomic transactions. Besides introducing the abstract semantics and programming model of HeTM, we present the design and evaluation of a concrete implementation of the proposed abstraction, referred herein as Speculative HeTM (SHeTM). SHeTM makes use of a novel design that leverages speculative techniques, which aims at hiding the inherently large communication latency between CPUs and discrete GPUs and at minimizing inter-device synchronization overhead. We demonstrate the efficiency of the SHeTM via an extensive quantitative study based both on synthetic benchmarks and on a popular object caching system.
ISSN:2641-7936
DOI:10.1109/PACT.2019.00026