Presented a talk about our value profiling tool at ASPLOS'22
General-purpose GPUs have become common in modern computing systems to accelerate applications in many domains, including machine learning, high-performance computing, and autonomous driving. However, inefficiencies abound in GPU-accelerated …
Graphics Processing Units (GPUs) have become a key technology for accelerating node performance in supercomputers, including the US Department of Energy’s forthcoming exascale systems. Since the execution model for GPUs differs from that for …
To address the challenge of performance analysis on the US DOE’s forthcoming exascale supercomputers, Rice University has been extending its HPCToolkit performance tools to support measurement and analysis of GPU-accelerated applications. To help …
Presented our CGO'21 work.
GPGPUs are widely used in high-performance computing systems to accelerate scientific and machine learning workloads. Developing efficient GPU kernels is critically important to obtain bare-metal performance on GPU-based clusters. In this paper, we …
This paper describes extensions to Rice University's HPCToolkit performance tools to support measurement and analysis of GPU-accelerated applications. To help developers understand the performance of accelerated applications as a whole, HPCToolkit's …