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SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads?
Jeffrey Jian Ma, Milad Hashemi, Amir Yazdanbakhsh +5
Optimizing the performance of large-scale software repositories demands expertise in code reasoning and software engineering (SWE) to reduce runtime while preserving program correc…
QuArch: A Benchmark for Evaluating LLM Reasoning in Computer Architecture
Shvetank Prakash, Andrew Cheng, Mark Mazumder +24
The field of computer architecture, which bridges high-level software abstractions and low-level hardware implementations, remains absent from current large language model (LLM) ev…
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…
A2Perf: Real-World Autonomous Agents Benchmark
Ikechukwu Uchendu, Jason Jabbour, Korneel Van den Berghe +15
Autonomous agents and systems cover a number of application areas, from robotics and digital assistants to combinatorial optimization, all sharing common, unresolved research chall…
QuArch: A Question-Answering Dataset for AI Agents in Computer Architecture
Shvetank Prakash, Andrew Cheng, Jason Yik +14
We introduce QuArch, a dataset of 1500 human-validated question-answer pairs designed to evaluate and enhance language models' understanding of computer architecture. The dataset c…