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cs.DC2025
Iris: First-Class Multi-GPU Programming Experience in Triton
Muhammad Awad, Muhammad Osama, Brandon Potter
Multi-GPU programming traditionally requires developers to navigate complex trade-offs between performance and programmability. High-performance implementations typically rely on l…
cs.DC2025
Eliminating Multi-GPU Performance Taxes: A Systems Approach to Efficient Distributed LLMs
Octavian Alexandru Trifan, Karthik Sangaiah, Muhammad Awad +5
As large language models (LLMs) continue to scale, their workloads increasingly rely on distributed execution across multiple GPUs. However, the conventional bulk synchronous paral…
cs.DC2025
SwizzlePerf: Hardware-Aware LLMs for GPU Kernel Performance Optimization
Arya Tschand, Muhammad Awad, Ryan Swann +5
Large language models (LLMs) have shown progress in GPU kernel performance engineering using inefficient search-based methods that optimize around runtime. Any existing approach la…