Jonathan Cohen and the NVIDIA CUDA Library Team present the latest benchmark results using the extensive numerical libraries included with CUDA 5. This webinar will cover all the data points and the significance of the new Math Library Performance Report
The main objective of the work presented in this paper is to compare implementations on FPGAs and CPUs of different fitness functions in evolutionary algorithms in order to study the performance of the floating-point arithmetic in FPGAs and CPUs that is often present in the optimization problems tackled by these algorithms.
We aim to deliver working, efficient GPU code in a library that is downloaded and run by many different users. The issue is to deliver efficiency independent of the individual user parameters and without a priori knowledge of the hardware the user will employ.
This whitepaper on OpenCL™ describing how to best utilize underlying Intel hardware architecture using OpenCL. This white paper will go over programming considerations for host-side device orchestration, as well as OpenCL kernels for CPU.
This paper presents a comprehensive performance comparison between CUDA and OpenCL. We have selected 16 benchmarks ranging from synthetic applications to real-world ones. We make an extensive analysis of the performance gaps taking into account programming models, optimization strategies, architectural details, and underlying compilers.
In this paper, we revisit the design of synchronization primitives-specifically barriers, mutexes, and semaphores-and how they apply to the GPU. Previous implementations are insufficient due to the discrepancies in hardware and programming model of the GPU and CPU.
We present a comprehensive study on the performance and power consumption of a recent ATI GPU. By employing a rigorous statistical model to analyze execution behaviors of representative general-purpose GPU (GPGPU) applications, we conduct insightful investigations on the target GPU architecture.
During the last decade, the performance and capabilities of graphics processing units (GPUs) have been drastically improved mostly due to the demands of the visualisation and the entertainment markets.