2 citations · 4 across the 5 of their papers we have counts for
5 papers
Pushing the Performance Envelope of DNN-based Recommendation Systems Inference on GPUs
Rishabh Jain, Vivek M. Bhasi, Adwait Jog +3
Personalized recommendation is a ubiquitous application on the internet, with many industries and hyperscalers extensively leveraging Deep Learning Recommendation Models (DLRMs) fo…
Enabling Software Resilience in GPGPU Applications via Partial Thread Protection
Lishan Yang, Bin Nie, Adwait Jog +1
Graphics Processing Units (GPUs) are widely used by various applications in a broad variety of fields to accelerate their computation but remain susceptible to transient hardware f…
Decoupling GPU Programming Models from Resource Management for Enhanced Programming Ease, Portability, and Performance
Nandita Vijaykumar, Kevin Hsieh, Gennady Pekhimenko +6
The application resource specification--a static specification of several parameters such as the number of threads and the scratchpad memory usage per thread block--forms a critica…
Improving Multi-Application Concurrency Support Within the GPU Memory System
Rachata Ausavarungnirun, Christopher J. Rossbach, Vance Miller +5
GPUs exploit a high degree of thread-level parallelism to hide long-latency stalls. Due to the heterogeneous compute requirements of different applications, there is a growing need…
A Framework for Accelerating Bottlenecks in GPU Execution with Assist Warps
Nandita Vijaykumar, Gennady Pekhimenko, Adwait Jog +7
Modern Graphics Processing Units (GPUs) are well provisioned to support the concurrent execution of thousands of threads. Unfortunately, different bottlenecks during execution and…