2 citations · 4 across the 9 of their papers we have counts for
9 papers
Quokka: Accelerating Program Verification with LLMs via Invariant Synthesis
Anjiang Wei, Tianran Sun, Tarun Suresh +3
Program verification relies on loop invariants, yet automatically discovering strong invariants remains a long-standing challenge. We investigate whether large language models (LLM…
Astra: A Multi-Agent System for GPU Kernel Performance Optimization
Anjiang Wei, Tianran Sun, Yogesh Seenichamy +5
GPU kernel optimization has long been a central challenge at the intersection of high-performance computing and machine learning. Efficient kernels are crucial for accelerating lar…
Mapple: A Domain-Specific Language for Mapping Distributed Programs
Anjiang Wei, Rohan Yadav, Hang Song +3
Optimizing parallel programs for distributed systems is a complex task, often requiring significant code modifications. Task-based programming systems improve modularity by separat…
SATBench: Benchmarking LLMs' Logical Reasoning via Automated Puzzle Generation from SAT Formulas
Anjiang Wei, Yuheng Wu, Yingjia Wan +6
We introduce SATBench, a benchmark for evaluating the logical reasoning capabilities of large language models (LLMs) through logical puzzles derived from Boolean satisfiability (SA…
SuperCoder: Assembly Program Superoptimization with Large Language Models
Anjiang Wei, Tarun Suresh, Huanmi Tan +4
Superoptimization is the task of transforming a program into a faster one, and ideally the very fastest possible one, while preserving its input-output behavior. In this work, we i…
VeriCoder: Enhancing LLM-Based RTL Code Generation through Functional Correctness Validation
Anjiang Wei, Huanmi Tan, Tarun Suresh +5
Recent advances in Large Language Models (LLMs) have sparked growing interest in applying them to Electronic Design Automation (EDA) tasks, particularly Register Transfer Level (RT…
CodeARC: Benchmarking Reasoning Capabilities of LLM Agents for Inductive Program Synthesis
Anjiang Wei, Tarun Suresh, Jiannan Cao +6
Inductive program synthesis, or programming by example, requires synthesizing functions from input-output examples that generalize to unseen inputs. While large language model agen…
EquiBench: Benchmarking Large Language Models' Reasoning about Program Semantics via Equivalence Checking
Anjiang Wei, Jiannan Cao, Ran Li +8
As large language models (LLMs) become integral to code-related tasks, a central question emerges: Do LLMs truly understand program semantics? We introduce EquiBench, a new benchma…
Improving Parallel Program Performance with LLM Optimizers via Agent-System Interfaces
Anjiang Wei, Allen Nie, Thiago S. F. X. Teixeira +4
Modern scientific discovery increasingly relies on high-performance computing for complex modeling and simulation. A key challenge in improving parallel program performance is effi…