Modern graphics processing units (GPUs) have complex architectures that admit
exceptional performance and energy efficiency for high-throughput applications. Although …

Tuning code for GPGPU and other emerging many-core platforms is a challenge because
few models or tools can precisely pinpoint the root cause of performance bottlenecks. In this …

GPUs have become prevalent and more general purpose, but GPU programming remains
challenging and time consuming for the majority of programmers. In addition, it is not always …

The gap between CPU and networking speeds has motivated the development of
SmartNICs for NF (network functions) offloading. However, offloading performance is …

GPU implementations of HPC applications relying on finite difference methods can include
tens of kernels that are memory-bound. Kernel fusion can improve performance by reducing …

In this paper, we present an approach to estimate GPU applications' performance upper
bound based on algorithm analysis and assembly code level benchmarking. As an example …

Graphics processing units (GPUs) can deliver considerable performance gains over general
purpose processors. However, GPU performance improvement vary considerably across …

Contemporary GPUs have significantly higher arithmetic throughput than a memory
throughput. Hence, many GPU kernels are memory bound and cannot exploit arithmetic …

The high performance computing landscape is shifting from collections of homogeneous
nodes towards heterogeneous systems, in which nodes consist of a combination of …

Analytical (predictive) application performance models are critical for diagnosing
performance-limiting resources, optimizing systems, and designing machines. Creating …