June 27, 2011
HPC aficionado Doug Eadline is doing a series of articles about alternative GPU programming methods over at Cluster Monkey. In this case, alternative means not CUDA or OpenCL, the two most popular frameworks for GPGPU programming today. Because of their low-level nature, both can be used to good effect to get optimal performance from the GPU -- perhaps CUDA more so than OpenCL -- but the languages are somewhat of a challenge for the average programmer.
Fortunately, there a number of other software development tools that have emerged to provide a higher level programming environment for the application developer. The first one Eadline tackles is PGI's accelerator model, a directives-based approach that can be used to turn Fortran or C source into GPU-accelerated code.
In a nutshell, the advantage to PGI's approach is that it allows programmers to instrument new or existing high-level source code using special directives that the compiler uses to generate GPU executable code. Conveniently the same source code can be used to generate vanilla CPU code if the GPU target is not available. In addition, the compiler removes some (but not all) of the data manipulation the programmer normally must do to manage separate memory spaces on the GPU and CPU.
The accelerator directives approach also has the advantage of hardware portability. The same source could theoretically be applied to NVIDIA and AMD GPUs, the upcoming Intel MIC coprocessor, or any other future accelerator. Currently PGI's accelerator compiler supports NVIDIA CUDA-capable GPUs and also has a CUDA-based port for multicore x86 CPUs.
Full story at Cluster Monkey
In quieter times, sounding the bell of funding big science with big systems tends to resonate further than when ears are already burning with sour economic and national security news. For exascale's future, however, the time could be ripe to instill some sense of urgency....
Read more...
In a recent solicitation, the NSF laid out needs for furthering its scientific and engineering infrastructure with new tools to go beyond top performance, Having already delivered systems like Stampede and Blue Waters, they're turning an eye to solving data-intensive challenges. We spoke with the agency's Irene Qualters and Barry Schneider about..
Read more...
Large-scale, worldwide scientific initiatives rely on some cloud-based system to both coordinate efforts and manage computational efforts at peak times that cannot be contained within the combined in-house HPC resources. Last week at Google I/O, Brookhaven National Lab’s Sergey Panitkin discussed the role of the Google Compute Engine in providing computational support to ATLAS, a detector of high-energy particles at the Large Hadron Collider (LHC).
Read more...
05/10/2013 | Cleversafe, Cray, DDN, NetApp, & Panasas | From Wall Street to Hollywood, drug discovery to homeland security, companies and organizations of all sizes and stripes are coming face to face with the challenges – and opportunities – afforded by Big Data. Before anyone can utilize these extraordinary data repositories, however, they must first harness and manage their data stores, and do so utilizing technologies that underscore affordability, security, and scalability.
04/15/2013 | Bull | “50% of HPC users say their largest jobs scale to 120 cores or less.” How about yours? Are your codes ready to take advantage of today’s and tomorrow’s ultra-parallel HPC systems? Download this White Paper by Analysts Intersect360 Research to see what Bull and Intel’s Center for Excellence in Parallel Programming can do for your codes.
In this demonstration of SGI DMF ZeroWatt disk solution, Dr. Eng Lim Goh, SGI CTO, discusses a function of SGI DMF software to reduce costs and power consumption in an exascale (Big Data) storage datacenter.
The Cray CS300-AC cluster supercomputer offers energy efficient, air-cooled design based on modular, industry-standard platforms featuring the latest processor and network technologies and a wide range of datacenter cooling requirements.