Loading...
Please wait, while we are loading the content...
Similar Documents
Cu2cl: a cuda-to-opencl translator for multi- and many-core architectures (2011).
| Content Provider | CiteSeerX |
|---|---|
| Author | Arroyo, Gabriel E. Martinez Sandu, Adrian |
| Abstract | The use of graphics processing units (GPUs) in high-performance parallel computing contin-ues to steadily become more prevalent, often as part of a heterogeneous system. For years, CUDA has been the de facto programming environment for nearly all general-purpose GPU (GPGPU) applications. In spite of this, the framework is available only on NVIDIA GPUs, traditionally requiring reimplementation in other frameworks in order to utilize additional multi- or many-core devices. On the other hand, OpenCL provides an open and vendor-neutral programming environment and run-time system. With implementations available for CPUs, GPUs, and other types of accelerators, OpenCL therefore holds the promise of a “write once, run anywhere ” ecosystem for heterogeneous computing. Given the many similarities between CUDA and OpenCL, manually porting a CUDA appli-cation to OpenCL is almost straightforward, albeit tedious and error-prone. In response to this issue, we created CU2CL, an automated CUDA-to-OpenCL source-to-source translator that possesses a novel design and clever reuse of the Clang compiler framework. Currently, the CU2CL translator covers the primary constructs found in the CUDA Runtime API, |
| File Format | |
| Publisher Date | 2011-01-01 |
| Access Restriction | Open |
| Subject Keyword | Many-core Architecture Cuda-to-opencl Translator Vendor-neutral Programming Environment Automated Cuda-to-opencl Source-to-source Translator Many Similarity Primary Construct General-purpose Gpu Clang Compiler Framework Cu2cl Translator Nvidia Gpus Cuda Appli-cation Clever Reuse Additional Multi Run-time System Many-core Device Novel Design Cuda Runtime Api Heterogeneous System |
| Content Type | Text |
