In this episode we'll go over an example of real-world code that has been parallelized by porting to the GPU. The use of shared memory to improve performance is covered as well as a discussion of synchronization points for coordinated work within a work-group. Source code is provided.David W. Gohara, Ph.D. által

This episode covers questions hthat were generated from the previous podcast. We'll discuss GPU layout/terminology and bank conflicts resulting from shared memory access.David W. Gohara, Ph.D. által

In this episode we cover some questions regarding function calls from kernels and the use of clFinish. Also, we'll discuss basic GPU architecture, memory layout, shared memory. Thread blocks, warps and efficient data loading will also be discussed.David W. Gohara, Ph.D. által

In this episode we cover some questions that were asked on the forums about double-precision arithmetic, object oriented programming, clarification on global and local work groups and types of scientific calculations that are amenable to GPU computing. In addition, we'll go over in more detail how to query devices for specific information and featu…

In this episode, we'll go over the fundamentals of OpenCL. Discussing concepts that once understood, will make implementing and using OpenCL much easier. This episode covers, OpenCL devices, objects and memory spaces. Several code examples are presented and we'll walk through the steps needed to run an OpenCL calculation.…

In this first episode, the Open Computing Language (OpenCL) will be introduced. Background information on what it is, why it's needed and how you can use it to help speed up numerically intensive calculations will be presented. There is demo showing OpenCL in use in a real world calculation.David W. Gohara, Ph.D. által