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With the increase in the complexity and number of nodes in large-scale high performance computing (HPC) systems, the probability of applications experiencing failures has increased significantly. As the computational demands of applications that execute on HPC systems increase, projections indicate that applications executing on exascale-sized systems are likely to operate with a mean time between...
The amount of data generated and collected across computing platforms every day is not only enormous, but growing at an exponential rate. Advanced data analytics and machinelearning techniques have become increasingly essential to analyze and extract meaning from such “Big Data”. These techniques can be very useful to detect patterns and trends to improve the operational behavior of computing platforms,...
As the computing power of large scale computing systems increases exponentially with time, their failure rates are increasing exponentially as well. While current high performance computing (HPC) systems experience failures of some type every few days, projections indicate that the next generation exascale machines will experience failures up to several times an hour. The resilience techniques implemented...
Multicore processors have become an integral part of modern large-scale and high-performance parallel and distributed computing systems. Unfortunately, applications co-located on multicore processors can suffer from decreased performance and increased dynamic energy use as a result of interference in shared resources, such as memory. As this interference is difficult to characterize, assumptions about...
As multicore processor architectures are now prevalent in server nodes of parallel and distributed computing systems, it has become important to characterize the performance of applications run on these architectures. This study investigates the performance degradation an application experiences from memory interference due to other applications colocated on cores of the same multicore processor....
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