Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
Concept
Compute kernel
Summary
In computing, a compute kernel is a routine compiled for high throughput accelerators (such as graphics processing units (GPUs), digital signal processors (DSPs) or field-programmable gate arrays (FPGAs)), separate from but used by a main program (typically running on a central processing unit). They are sometimes called compute shaders, sharing execution units with vertex shaders and pixel shaders on GPUs, but are not limited to execution on one class of device, or graphics APIs.
Compute kernels roughly correspond to inner loops when implementing algorithms in traditional languages (except there is no implied sequential operation), or to code passed to internal iterators.
They may be specified by a separate programming language such as "OpenCL C" (managed by the OpenCL API), as "compute shaders" written in a shading language (managed by a graphics API such as OpenGL), or embedded directly in application code written in a high level language, as in the case of C++AMP.
This programming paradigm maps well to vector processors: there is an assumption that each invocation of a kernel within a batch is independent, allowing for data parallel execution. However, atomic operations may sometimes be used for synchronization between elements (for interdependent work), in some scenarios. Individual invocations are given indices (in 1 or more dimensions) from which arbitrary addressing of buffer data may be performed (including scatter gather operations), so long as the non-overlapping assumption is respected.
The Vulkan API provides the intermediate SPIR-V representation to describe both Graphical Shaders, and Compute Kernels, in a language independent and machine independent manner. The intention is to facilitate language evolution and provide a more natural ability to leverage GPU compute capabilities, in line with hardware developments such as Unified Memory Architecture and Heterogeneous System Architecture. This allows closer cooperation between a CPU and GPU.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Ontological neighbourhood
Related courses (2)
COM-490: Large-scale data science for real-world data
This hands-on course teaches the tools & methods used by data scientists, from researching solutions to scaling up
prototypes to Spark clusters. It exposes the students to the entire data science pipe
CS-307: Introduction to multiprocessor architecture
Multiprocessors are a core component in all types of computing infrastructure, from phones to datacenters. This course will build on the prerequisites of processor design and concurrency to introduce
Related publications (33)