The SGI Management Suite’s system health monitoring and management capability collects health status information on fundamental system’s functions such as memory, CPU and power. It identifies changes that require action, automatically alerts the system administrator, and provides proactive solutions to correct the problem.
Organizations that implement high-performance computing (HPC) technologies have a wide range of requirements. From small manufacturing suppliers to national research institutions, using significant computing technologies is critical to creating innovative products and leading-edge research. No two HPC installations are the same. “For maximum return, budget, software requirements, performance and customization all must be considered before installing and operating a successful environment.”
Pressures by management for cost containment are answered by improving software maintenance procedures and automating many of the repetitive activities that have been handled manually. This lowers Total Cost of Ownership (TCO), boosting IT productivity, and increasing return on investment (ROI).
While all users of HPC technology want the fastest performance available, price and power consumption always seem to come into play, whether in the initial planning or at a later time. Standard performance measures exist that may or may not relate to an end user’s application mix, but it is important to understand the various benchmark results that go into determining the performance of a CPU, a server or an overall cluster.
While HPC has its roots in academia and government where extreme performance was the primary goal, high performance computing has evolved to serve the needs of businesses with sophisticated monitoring, pre-emptive memory error detection, and workload management capabilities. This evolution has enabled “production supercomputing,” where resilience can be sustained without sacrificing performance and job throughput.
Today’s High Performance Computing (HPC) systems offer the ability to model everything from proteins to galaxies. The insights and discoveries offered by these systems are nothing short of astounding. Indeed, the ability to process, move, and store data at unprecedented levels, often reducing jobs from weeks to hours, continues to move science and technology forward at an accelerating pace. This article series offers those considering HPC, both users and managers, guidance when considering the best way to deploy an HPC solution.
Successful HPC computing depends on choosing the architecture that addresses both application and institutional needs. In particular, finding a simple path to leading edge HPC and Data Analytics is not difficult, if you consider the capabilities and limitations of various approaches to HPC performance, scaling, ease of use, and time to solution. Careful analysis and consideration of the following questions will help lead to a successful and cost-effective HPC solution. Here are three questions to ask to ensure HPC success.
Cloud computing has become another tool for the HPC practitioner. For some organizations, the ability of cloud computing to shift costs from capital to operating expenses is very attractive. Because all cloud solutions require use of the Internet, a basic analysis of data origins and destinations is needed. Here’s an overview of when local or cloud HPC make the most sense.
Parallel file systems have become the norm for HPC environments. While typically used in high end simulations, these parallel file systems can greatly affect the performance and thus the customer experience when using analytics from leading organizations such as SAS. This whitepaper is an excellent summary of how parallel file systems can enhance the workflow and insight that SAS Analytics gives.
Many HPC applications began as single processor (single core) programs. If these applications take too long on a single core or need more memory than is available, they need to be modified so they can run on scalable systems. Fortunately, many of the important (and most used) HPC applications are already available for scalable systems. Not all applications require large numbers of cores for effective performance, while others are highly scalable. Here is how to better understand your HPC application needs.