Design and analysis of CXL performance models for tightly-coupled heterogeneous computing

Cabrera, Anthony M.; Young, Aaron; Vetter, Jeffrey S.

doi:10.1145/3529336.3530817

Cited by 5 publications

(1 citation statement)

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“…Analysis and evaluation of CXL-enabled systems are being conducted ranging from memory pooling in general [12,23,41,44,46], to more specific applications such as machine learning [19] and in-memory databases [21]. CXL studies involving accelerators such as GPU and FPGA are appearing [3,19]. Our work complements these studies and deals with GPU graph processing on CXL memory for the first time.…”

Section: Related Workmentioning

confidence: 99%

GPU Graph Processing on CXL-Based Microsecond-Latency External Memory

Sano,

Bando,

Hiwada

et al. 2023

Proceedings of the SC '23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analys

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In GPU graph analytics, the use of external memory such as the host DRAM and solid-state drives is a cost-effective approach to processing large graphs beyond the capacity of the GPU onboard memory. This paper studies the use of Compute Express Link (CXL) memory as alternative external memory for GPU graph processing in order to see if this emerging memory expansion technology enables graph processing that is as fast as using the host DRAM. Through analysis and evaluation using FPGA prototypes, we show that representative GPU graph traversal algorithms involving finegrained random access can tolerate an external memory latency of up to a few microseconds introduced by the CXL interface as well as by the underlying memory devices. This insight indicates that microsecond-latency flash memory may be used as CXL memory devices to realize even more cost-effective GPU graph processing while still achieving performance close to using the host DRAM. CCS CONCEPTS• Hardware → Analysis and design of emerging devices and systems; Memory and dense storage.

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