CTA-pipelining reduces 2-layer GEMM latency up to 31.8% vs micro-batching and 29.6% vs Tensor Parallelism on 8-GPU H200/B200 systems by CTA-level cross-GPU pipelining.
A sample-free compilation framework for efficient dynamic tensor computation
4 Pith papers cite this work, alongside 5 external citations. Polarity classification is still indexing.
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ANNS-AMP adapts distance-computation precision to vector-space regions via a lightweight cluster-level predictor and a bit-serial accelerator, delivering 163.76x/10.57x/2.06x average speedups and 1100x/39.41x/6.66x energy reductions versus CPU/GPU/custom baselines with <2.7% accuracy loss.
FalconGEMM delivers a framework with deployment, group-parallel execution, and analytical decision modules that makes lower-complexity matrix multiplication practical, beating cuBLAS and similar libraries by 7.59-17.85% on LLM tasks.
Co-design of 14.5x compacted index, asynchronous scheduler, and multiplication-free kernel for PIM-based graph ANNS delivers up to 20x CPU and 17.1x GPU throughput on billion-scale benchmarks.
citing papers explorer
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CTA-Pipelining: A Latency-Oriented Spatial Scaling Method for Multi-GPU Systems
CTA-pipelining reduces 2-layer GEMM latency up to 31.8% vs micro-batching and 29.6% vs Tensor Parallelism on 8-GPU H200/B200 systems by CTA-level cross-GPU pipelining.
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ANNS-AMP: Accelerating Approximate Nearest Neighbor Search via Adaptive Mixed-Precision Computing
ANNS-AMP adapts distance-computation precision to vector-space regions via a lightweight cluster-level predictor and a bit-serial accelerator, delivering 163.76x/10.57x/2.06x average speedups and 1100x/39.41x/6.66x energy reductions versus CPU/GPU/custom baselines with <2.7% accuracy loss.
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FalconGEMM: Surpassing Hardware Peaks with Lower-Complexity Matrix Multiplication
FalconGEMM delivers a framework with deployment, group-parallel execution, and analytical decision modules that makes lower-complexity matrix multiplication practical, beating cuBLAS and similar libraries by 7.59-17.85% on LLM tasks.
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Co-Designing Graph-based Approximate Nearest Neighbor Search at Billion Scale for Processing-in-Memory
Co-design of 14.5x compacted index, asynchronous scheduler, and multiplication-free kernel for PIM-based graph ANNS delivers up to 20x CPU and 17.1x GPU throughput on billion-scale benchmarks.