3 papers
cs.DC2026
Xe-Forge: Multi-Stage LLM-Powered Kernel Optimization for Intel GPU
Marcin Spoczynski, Daniel Fleischer, Moshe Berchansky +5
Porting deep learning algorithms to new hardware accelerators requires developers to repeatedly apply the same low-level optimizations -- quantization, memory access coalescing, ti…
cs.LG2025
Library Liberation: Competitive Performance Matmul Through Compiler-composed Nanokernels
Arun Thangamani, Md Asghar Ahmad Shahid, Adam Siemieniuk +3
The rapidly evolving landscape of AI and machine learning workloads has widened the gap between high-level domain operations and efficient hardware utilization. Achieving near-peak…
cs.PL2024
Towards a high-performance AI compiler with upstream MLIR
Renato Golin, Lorenzo Chelini, Adam Siemieniuk +5
This work proposes a compilation flow using open-source compiler passes to build a framework to achieve ninja performance from a generic linear algebra high-level abstraction. We d…