ScholarWorks@성균관대학교

초록

Address translation has become a significant and growing performance bottleneck in modern GPUs, especially for emerging irregular applications with high TLB miss rates. The limited concurrency of hardware Page Table Walkers (PTWs), due to their small and fixed number, causes severe contention and substantial queueing delays under high translation pressure, which significantly degrades performance. This paper introduces SoftWalker, a novel, scalable, and flexible framework that fundamentally shifts the GPU page table walking from fixed-function hardware to software execution. SoftWalker leverages the GPU's massive thread-level parallelism by dynamically dispatching specialized, lightweight software threads running on GPU cores to handle TLB misses requiring page table walks. In addition, to expand L2 TLB MSHR capacity on demand, SoftWalker incorporates In-TLB MSHRs, a key innovation that repurposes underutilized L2 TLB entries to track outstanding misses when existing MSHRs are saturated. By alleviating MSHR-induced contention, this design preserves the key advantage of highly parallel page table walking in software. SoftWalker enables thousands of concurrent page table walks, significantly reducing PTW-level contention and translation queueing delays. As a result, it achieves an average reduction of 72.8% in page walk latency and delivers an average speedup of 2.24 × (3.94 × for irregular workloads).

키워드