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.
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BatchLLM achieves 1.3x-10.8x higher throughput than vLLM and SGLang for batched LLM inference with prefix sharing via global prefix identification, decoding-first reordering, and memory-centric token batching.
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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.
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BatchLLM: Optimizing Large Batched LLM Inference with Global Prefix Sharing and Throughput-oriented Token Batching
BatchLLM achieves 1.3x-10.8x higher throughput than vLLM and SGLang for batched LLM inference with prefix sharing via global prefix identification, decoding-first reordering, and memory-centric token batching.