In high performance computing systems, object deserialization can become a surprisingly
important bottleneck---in our test, a set of general-purpose, highly parallelized applications …

This paper proposes a fully autonomous execution model for multi-GPU applications that
completely excludes the involvement of the CPU beyond the initial kernel launch. In a typical …

In recent years, GPUs have become the preferred accelerators for HPC and ML applications
due to their parallelism and fast memory bandwidth. While GPUs boost computation, inter …

Endpoint congestion in HPC networks creates tree saturation that is detrimental to
performance. Endpoint congestion can be alleviated by reducing the injection rate of traffic …

Over the last decade, CUDA and the underlying GPU hardware architecture have
continuously gained popularity in various high-performance computing application domains …

Due to their massive parallelism and high performance per Watt, GPUs have gained high
popularity in high-performance computing and are a strong candidate for future exascale …

Current state-of-the-art in GPU networking utilizes a host-centric, kernel-boundary
communication model that reduces performance and increases code complexity. To address …

GPUDirect RDMA (GDR) brings the high-performance communication capabilities of RDMA
networks like InfiniBand (IB) to GPUs (referred to as" Device"). It enables IB network …

Accelerators, such as GPUs, have proven to be highly successful in reducing execution time
and power consumption of compute-intensive applications. Even though they are already …

GPUs are widespread across clusters of compute nodes due to their attractive performance
for data parallel codes. However, communicating between GPUs across the cluster is …