A CXL-attached SSD using Determinism and Bufferability annotations is simulated to be 10.9x faster than PCIe memory expansion and to approach DRAM-like latency under high locality.
UDON: A case for offloading to general purpose compute on CXL memory
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abstract
Upcoming CXL-based disaggregated memory devices feature special purpose units to offload compute to near-memory. In this paper, we explore opportunities for offloading compute to general purpose cores on CXL memory devices, thereby enabling a greater utility and diversity of offload. We study two classes of popular memory intensive applications: ML inference and vector database as candidates for computational offload. The study uses Arm AArch64-based dual-socket NUMA systems to emulate CXL type-2 devices. Our study shows promising results. With our ML inference model partitioning strategy for compute offload, we can place up to 90% data in remote memory with just 20% performance trade-off. Offloading Hierarchical Navigable Small World (HNSW) kernels in vector databases can provide upto 6.87$\times$ performance improvement with under 10% offload overhead.
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From Block to Byte: Transforming PCIe SSDs with CXL Memory Protocol and Instruction Annotation
A CXL-attached SSD using Determinism and Bufferability annotations is simulated to be 10.9x faster than PCIe memory expansion and to approach DRAM-like latency under high locality.