High Performance Computing and Big Data analytics touch us every day. We each rely on daily weather forecasts, banking and financial information, scientific and health analyses, and thousands of other activities that involve HPC and Big Data analysis.
Designating the appropriate provider for large MPI applications is critical to taking advantage of all of the compute power available. “A modern HPC system with multiple host cpus and multiple coprocessors such as the Intel Xeon Phi coprocessor housed in numerous racks can be optimized for maximum application performance with intelligent thread placement.”
“The combination of using a host cpu such as an Intel Xeon combined with a dedicated coprocessor such as the Intel Xeon Phi coprocessor has been shown in many cases to improve the performance of an application by significant amounts. When the datasets are large enough, it makes sense to offload as much of the workload as possible. But is this the case when the potential offload data sets are not as large?”
With the growth of big data, cloud and high performance computing, demands on data centers around the world are expanding every year. Unfortunately, these demands are coming up against significant opposition in the form of operating constraints, capital constraints, and sustainability goals. In this article, we look at 8 of these constraints and how direct-to-chip liquid cooling is solving them.
As design challenges become more complex and time to product launches are reduced, it is important to understand how to use a cluster for simulation, as compared to just a single node. “HPC Clusters Drive Design Optimization” is an excellent introduction on how to get the most out of a compute cluster.
“N-Body problems compare the interaction of N-bodies against N-bodies, which results in calculations of the order of N2. As this can be computationally very expensive, but a well understood process, techniques and optimizations can be performed on application code using compiler directives and easy to understand techniques.”