5 papers
A New Lens on Multi-GPU Communication: API-Level Memory Models for Portable Optimization
Johnathan Alsop, Bradford Beckmann, Brandon Potter +5
As network communication increasingly becomes a performance bottleneck for ML and HPC workloads, it is more important than ever to optimize multi-GPU data transfers. Recently, low-…
Toward a Unified GPU-Aware OpenSHMEM Specification
Naveen Ravi, Nathan Wichmann, Md. Wasi-ur- Rahman +19
Leadership-class HPC systems are now accelerator-centric, with GPUs providing most floating-point throughput and memory bandwidth. As next-generation systems increasingly integrate…
A Case for Fine-grain Coherence Specialization in Heterogeneous Systems
Johnathan Alsop, Weon Taek Na, Matthew D. Sinclair +2
Hardware specialization is becoming a key enabler of energyefficient performance. Future systems will be increasingly heterogeneous, integrating multiple specialized and programmab…
Specializing Coherence, Consistency, and Push/Pull for GPU Graph Analytics
Giordano Salvador, Wesley H. Darvin, Muhammad Huzaifa +3
This work provides the first study to explore the interaction of update propagation with and without fine-grained synchronization (push vs. pull), emerging coherence protocols (GPU…
Optimizing GPU Cache Policies for MI Workloads
Johnathan Alsop, Matthew D. Sinclair, Srikant Bharadwaj +8
In recent years, machine intelligence (MI) applications have emerged as a major driver for the computing industry. Optimizing these workloads is important but complicated. As memor…