AMD helps OpenCL gain ground in HPC space
September 28, 2011 —
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Related Search Term(s): AMD, HPC, nVidia, OpenCL
When it comes to high-performance computing applications, OpenMP has long been the standard open-source API for the job. But with new processors and new efforts from the companies behind those processors, OpenCL [Open Compute Language] has emerged as a new challenger in the HPC space. According to Evans' Data Corp., OpenCL is now the second most popular HPC tool, behind Intel's Threading Building Blocks. Evans also shows OpenCL adoption has increased since 2009.
AMD is preparing a retinue of new tools for OpenCL developers, tools it hopes will help to spread the adoption of this open framework for writing heterogeneous cluster-based applications. The aim is to compete with nVidia's CUDA tools and framework.
The most significant and recent change to both platforms has been the unification of memory space across RAM and VRAM, allowing developers to track the stored information for their high-performance compute applications without having to use two separate maps for memory.
Now that the two systems are nearing par with each other, AMD has decided to step up its game by releasing optimization and development tools that fill in the gaps it sees in the OpenCL ecosystem.
Further proof of AMD's newfound commitment to tools and HPC came last May 2010, when Manju Hegde left nVidia to join AMD. Hegde became AMD's corporate vice president of its products group.
“The promise of OpenCL is that you can optimize,” he said. “To give meaning to that, we've developed tools.” He went on to say that much of the work in HPC is not writing the functionality, but streamlining the code to be as fast as possible.
“OpenCL's promise is that it works across platforms," said Hegde. "It works across CPUs, GPUs and in the low-power space. It works across vendors. To give meaning to that message, the first thing we've done is invested in tools that allow development across CPU and GPU."
Right tools for the job
Those tools run the gamut from debuggers to performance profilers. The gDEbugger is designed to help developers find trouble spots in their applications, and to help them identify bottlenecks. AMD Code Analyst, on the other hand, is designed to explicitly point out those bottlenecks to developers.