Presentation
AGILE: Lightweight and Efficient Asynchronous GPU-SSD Integration
SessionProgramming Frameworks
DescriptionGraphics processing units have become essential for computationally intensive applications. However, emerging workloads often involve processing data exceeding GPU on-chip memory capacity. To mitigate this issue, existing solutions enable GPUs to use CPU DRAM or SSDs as external memory. Among them, the GPU-centric approach lets GPU threads directly access SSDs, eliminating CPU intervention overhead over traditional methods. However, the existing work adopts a synchronous model, and threads must tolerate the long communication latency before starting any tasks.
In this work, we propose AGILE, a lightweight and efficient asynchronous library allowing GPU threads to access SSDs asynchronously. We demonstrate that AGILE achieves up to 1.88x improvement in workloads with different CTCs. Additionally, AGILE achieves 1.75x performance improvement on DLRMs against the SOTA work BaM. AGILE also exhibits low API overhead on graph applications. Lastly, AGILE consumes fewer registers and requires up to 1.32x fewer registers in CUDA kernels.
In this work, we propose AGILE, a lightweight and efficient asynchronous library allowing GPU threads to access SSDs asynchronously. We demonstrate that AGILE achieves up to 1.88x improvement in workloads with different CTCs. Additionally, AGILE achieves 1.75x performance improvement on DLRMs against the SOTA work BaM. AGILE also exhibits low API overhead on graph applications. Lastly, AGILE consumes fewer registers and requires up to 1.32x fewer registers in CUDA kernels.
Event Type
Paper
TimeWednesday, 19 November 202510:52am - 11:15am CST
Location275
Programming Frameworks