  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 fine-grained 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.
