6 papers
Partitioning Unstructured Sparse Tensor Algebra for Load-Balanced Parallel Execution
Atharva Chougule, Alexander J Root, Rubens Lacouture +3
Sparse tensor algebra is challenging to efficiently parallelize due to the irregular, data-dependent, and potentially skewed structure of sparse computation. We propose the first p…
Optimal Software Pipelining and Warp Specialization for Tensor Core GPUs
Rupanshu Soi, Rohan Yadav, Fredrik Kjolstad +4
GPU architectures have continued to grow in complexity, with recent incarnations introducing increasingly powerful fixed-function units for matrix multiplication and data movement…
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…
On the Duality of Task and Actor Programming Models
Rohan Yadav, Joseph Guman, Sean Treichler +4
Programming models for distributed and heterogeneous machines are rapidly growing in popularity to meet the demands of modern workloads. Task and actor models are common choices th…
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…
Task-Based Tensor Computations on Modern GPUs
Rohan Yadav, Michael Garland, Alex Aiken +1
Domain-specific, fixed-function units are becoming increasingly common in modern processors. As the computational demands of applications evolve, the capabilities and programming i…