Exploring the multiple-gpu design space

Graphics Processing Units (GPUs) have been growing in popularity due to their impressive processing capabilities, and with general purpose programming languages such as NVIDIA’s CUDA interface, are becoming the platform of choice in the scientific computing community. Previous studies that used GPUs focused on obtaining significant performance gains from execution on a single GPU. These studies employed low-level, architecture-specific tuning in order to achieve sizeable benefits over multicore CPU execution. In this paper, we consider the benefits of running on multiple (parallel) GPUs to provide further orders of performance speedup. Our methodology allows developers to accurately predict execution time for GPU applications while varying the number and configuration of the GPUs, and the size of the input data set. This is a natural next step in GPU computing because it allows researchers to determine the most appropriate GPU configuration for an application without having to purchase hardware, or write the code for a multiple-GPU implementation. When used to predict performance on six scientific applications, our framework produces accurate performance estimates (11 % difference on average and 40 % maximum difference in a single case) for a range of short and long running scientific programs. 1

Heterogeneous computing with OpenCL 2.0

Heterogeneous Computing with OpenCL 2.0 teaches OpenCL and parallel programming for complex systems that may include a variety of device architectures: multi-core CPUs, GPUs, and fully-integrated Accelerated Processing Units (APUs). This fully-revised edition includes the latest enhancements in OpenCL 2.0 including: Shared virtual memory to increase programming flexibility and reduce data transfers that consume resources Dynamic parallelism which reduces processor load and avoids bottlenecks Improved imaging support and integration with OpenGL  Designed to work on multiple platfo

Heterogeneous Computing with OpenCL

Heterogeneous Computing with OpenCL teaches OpenCL and parallel programming for complex systems that may include a variety of device architectures: multi-core CPUs, GPUs, and fully-integrated Accelerated Processing Units (APUs) such as AMD Fusion technology. Designed to work on multiple platforms and with wide industry support, OpenCL will help you more effectively program for a heterogeneous future. Written by leaders in the parallel computing and OpenCL communities, this book will give you hands-on OpenCL experience to address a range of fundamental parallel algorithms. The authors explore