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Intel Gives Code Modernization Fresh Push

May 28, 2014 |

In the conversations leading up to exascale, one of the most frequently cited pain points is the need for massive software optimization and code modernization. But this isn’t just a relevant topic for the largest system operators at the supercomputing pinnacle. According to Intel’s General Manager of the Technical Computing Group, Charlie Wuischpard, there are Read more…

Altair Crash Tests New Xeon E7s

Mar 12, 2014 |

RADIOSS, one of the top structural analysis solvers in the industry, is used by auto companies like Ford and PSA in France to run car crash and other simulations to test safety and viability of a broad array of products. Like similar CAE codes, it is highly parallelized for top performance on large, powerful clusters–and Read more…

The Future of Accelerator Programming

Jan 9, 2014 |

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…

Adding MUSCLE to Multiscale Simulations

Dec 11, 2013 |

Multiscale models help understand phenomena with a wider scope or an increased level of detail. These models allow us to take the best from multiple worlds, for example by combining models with a fine-grained time or space resolution with models that capture systems over a large baseline. Classical examples of multiscale modeling include coupling atomistic Read more…

Compilers and More: Accelerated Programming

Dec 3, 2013 |

Having just returned from SC13, one burning issue is the choice of a standard approach for programming the next generation HPC systems. While not guaranteed, these systems are likely to be large clusters of nodes with multicore CPUs and some sort of attached accelerators. A standard programming approach is necessary to convince developers, and particularly Read more…

Short Takes

Evolving Exascale Applications Via Graphs

Apr 29, 2014 |

There is little point to building expensive exaflop-class computing machines if applications are not available to exploit the tremendous scale and parallelism. Consider that exaflop-class supercomputers will exhibit billion-way parallelism, and that calculations will be restricted by energy consumption, heat generation, and data movement. This level of complexity is sufficient to stymy application development, which Read more…

Prioritizing Data in the Age of Exascale

Apr 14, 2014 |

By now, most HPCers and the surrounding community are aware that data movement poses one of the most fundamental challenges to post-petascale computing. Around the world exascale-directed projects are attempting to maximize system speeds while minimizing energy costs. In the US, for example, exascale targets have peak performance increasing by three orders of magnitude while Read more…

Simulating HPC Workload Energy Costs

Feb 21, 2014 |

In today’s computing world, energy- and power-efficiency in data centers is critical to reducing a system’s overall total cost of ownership. Energy-efficiency is so important that the energy cost of operating a datacenter far exceeds its initial capital investment. In addition to cost savings, improvements in energy-efficiency also translate into lower carbon emissions. As Omar Read more…

Unleashing The Potential of OpenMP via Bottleneck Analysis

Feb 13, 2014 |

To capitalize on the computational potential of parallel processors, programmers must identify bottlenecks that limit their application. These bottlenecks typically chain performance preventing an application from reaching its full potential. Performance analysis typically provides the data and insight necessary to identify opportunities for program optimization. Researchers in the Inderprastha Engineering College identify general bottlenecks for Read more…

Scientific Computing: the Case for Python

Jan 10, 2014 |

What’s in your scientific computing toolbox? Over at the R Bloggers site, University of Texas at Austin research associate Tal Yarkoni explains why these days, his go-to language is Python, whether it’s for text processing, numerical computing, or even data visualization. The post, subtitled “why Python is steadily eating other languages’ lunch,” explores the advantages Read more…

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