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As we move towards the Exactable era of supercomputing, node-level failures are becoming more common-place, frequent check pointing is currently used to recover from such failures in long-running science applications. While compute performance has steadily improved year-on-year, parallel I/O performance has stalled, meaning check pointing is fast becoming a bottleneck to performance. Using current...
Work stealing is an effective load balancing technique in shared memory parallel programming. However, in a distributed setup researchers have pointed out difficulties in termination detection and in sustaining a healthy steal success rate. Keeping unsuccessful steal attempts to a minimum is especially important with many-core accelerators (having specialized engines for data copy-in and copy-out),...
High Performance Computing (HPC) involves solving intensive problems beyond desktop computing. Teaching HPC to undergraduate students requires special consideration of taking students comprehension to clusters of parallel machines - which can solve intensive tasks. In addition, the concepts of HPC should be well-aided with programming examples and assignments, in order make them understand the concepts,...
Many petascale and exascale scientific simulations involve the time evolution of systems modelled as Partial Differential Equations (PDEs). The sparse grid combination technique (SGCT) is a cost-effective method for solve time-evolving PDEs, especially for higher-dimensional problems. It consists of evolving PDE over a set of grids of differing resolution in each dimension, and then combining the...
Power is increasingly the limiting factor in High Performance Computing (HPC). Growing core counts in each generation increase power and energy demands. In the future, strict power and energy budgets will be used to control the operating costs of supercomputer centers. Every node needs to use energy wisely. Energy efficiency can either be improved by taking less time or running at lower power. In...
The complexity of today's high performance computing systems, and their parallel software, requires performance analysis tools to fully understand application performance behavior. The visualization of event streams has proven to be a powerful approach for the detection of various types of performance problems. However, visualization of large numbers of process streams quickly hits the limits of available...
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