Many of the latest supercomputers are based on accelerators, including the two fastest systems according to the 11/2013 TOP500 list. Accelerators are also becoming widespread in PCs and are even starting to appear in handheld devices, which will further boost the interest in accelerator programming. This broad adoption is the result of high performance, good Read more…
As the non-profit standards group behind the push for wider adoption via easier use of accelerators, OpenACC has quite a big job ahead. Although analysts agree that accelerators sit along a comfortable adoption curve, usability, programmability and portability are key concerns, among others. Over the last couple of years, OpenACC has worked with user groups Read more…
Moments ago, NVIDIA announced its acquisition of the Portland Group (PGI) which has provided compiler and tools for the HPC-oriented C and Fortran markets. According to the company’s Sumit Gupta, this will allow them to further build their software portfolio and to push the adoption of GPUs through OpenACC in particular. NVIDIA and PGI will…
<img style=”float: left;” src=”http://media2.hpcwire.com/hpcwire/OpenMP_logo_small.bmp” alt=”” width=”112″ height=”36″ />OpenMP, the popular parallel programming standard for high performance computing, is about to come out with a new version incorporating a number of enhancements, the most significant one being support for HPC accelerators. Version 4.0 will include the functionality that was implemented in OpenACC, the accelerator API that splintered off from the OpenMP work, as well as offer additional support beyond that. The new standard is expected to become the the law of the land sometime in early 2013.
Hyper-Q feature designed to make MPI run faster than ever before.
<img style=”float: left;” src=”http://media2.hpcwire.com/hpcwire/Earth_ocean_simulation.bmp” alt=”” width=”128″ height=”86″ />In a report published this week, researchers documented that GPU-equipped supercomputers enabled application speedups between 1.4x and 6.1x across a range of well-known science codes. While those results aren’t the order of magnitude performance increases that were being bandied about in the early days of GPU computing, the researchers were encouraged that the technology is producing consistently good results with some of the most popular HPC science applications in the world.
<img style=”float: left;” src=”http://media2.hpcwire.com/hpcwire/OpenACC_logo.bmp” alt=”” width=”139″ height=”47″ />PGI, Cray, and CAPS enterprise are moving quickly to get their new OpenACC-supported compilers into the hands of GPGPU developers. At NVIDIA’s GPU Technology Conference this week, there was plenty of discussion around the new HPC accelerator framework, and all three OpenACC compiler makers, as well as NVIDIA, were talking up the technology.
<img style=”float: left;” src=”http://media2.hpcwire.com/hpcwire/knights_corner_small.JPG” alt=”” width=”105″ height=”87″ />As NVIDIA’s upcoming Kepler-grade Tesla GPU prepares to do battle with Intel’s Knight Corner, the companies are busy formulating their respective HPC accelerator stories. While NVIDIA has enjoyed the advantage of actually having products in the field to talk about, Intel has managed to capture the attention of some fence-sitters with assurances of high programmability, simple recompiles, and transparent scalability for its Many Integrated Core (MIC) coprocessors. But according to NVIDIA’s Steve Scott, such promises ignore certain hard truths about how accelerator-based computing really works.
Lost in the flotilla of vendor news at the Supercomputing Conference (SC11) in Seattle last month was the announcement of a new directives-based parallel programming standard for accelerators. Called OpenACC, the open standard is intended to bring GPU computing into the realm of the average programmer, while making the resulting code portable across other accelerators and even multicore CPUs.