PrISM uses a Sampled History Queue to correlate row samples across windows, solving the non-selection problem in probabilistic RowHammer mitigation and cutting slowdown from 10.7% to 1.5% at threshold 250 versus prior methods.
AsmDB: Understanding and Mitigating Front-End Stalls in Warehouse-Scale Computers
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IP-CaT jointly optimizes TLB and cache management for L1I prefetching via a translation prefetch buffer and trimodal replacement policy, yielding 8.7% geomean speedup over EPI across 105 server workloads.
WaveTune introduces a wave-aware bilinear latency predictor and wave-structured sparse sampling to enable fast runtime auto-tuning of GPU kernels, achieving up to 1.83x kernel speedup and 1.33x TTFT reduction with drastically lower overhead.
citing papers explorer
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Loaded Dice: Solving the Non-Selection Problem for Scalable Probabilistic RowHammer Defense
PrISM uses a Sampled History Queue to correlate row samples across windows, solving the non-selection problem in probabilistic RowHammer mitigation and cutting slowdown from 10.7% to 1.5% at threshold 250 versus prior methods.
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Enhancing Instruction Prefetching via Cache and TLB Management
IP-CaT jointly optimizes TLB and cache management for L1I prefetching via a translation prefetch buffer and trimodal replacement policy, yielding 8.7% geomean speedup over EPI across 105 server workloads.
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WaveTune: Wave-aware Bilinear Modeling for Efficient GPU Kernel Auto-tuning
WaveTune introduces a wave-aware bilinear latency predictor and wave-structured sparse sampling to enable fast runtime auto-tuning of GPU kernels, achieving up to 1.83x kernel speedup and 1.33x TTFT reduction with drastically lower overhead.