The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
We present direct performance measurement for eight popular HPC applications on the Knights Landing (KNL) platform. Performance numbers for Haswell processors are provided for contrast. The applications (DGEMM. SGEMM, STREAM, IOR, HPCG, Quantum Espresso, WRF and HPL) were selected from among the ten most used in the QCT developer cloud as well as good representative of workloads used by large number...
It is commonly the case that a small number of widely used applications make up a large fraction of the workload of HPC centers. Predicting the performance of important applications running on specific processors enables HPC centers to design best performing system configurations and to insure good performance for the most popular applications on new systems. In the analyses presented in this paper...
At present, the cost of High Performance Computing (HPC) system is very high and recently cloud has been popular for its operation on HPC. In this study, we investigated the price efficiency that will benefit the customer in choosing provider's package. We analyzed relation between prices, times and problem sizes (N) or workloads from High Performance Linpack on Amazon EC2 in Compute Optimize package...
Performance tuning is an ongoing activity at most HPC sites. Small performance improvements can save thousands of dollars. Run-to-run performance variations significantly impact performance tuning. Not being able to tell which code version is faster (or more energy efficient) in a single run greatly increases the computational expense and uncertainty for theprogrammer. We will show examples where...
The cloud resources can be efficiently utilized using different parallelization methods and techniques. However, most of them depend on the operating system and its runtime environment. In this paper we perform series of experiments to analyze the performance of dense matrix-matrix multiplication algorithm on the same hardware infrastructure using parallel threads on different platforms in Windows...
Dense matrix-matrix multiplication algorithm is widely used in large scientific applications, and often it is an important factor of the overall performance of the application. Therefore, optimizing this algorithm, both for parallel and serial execution would give an overall performance boost. In this paper we overview the most used dense matrix multiplication optimization techniques applicable for...
Virtualization technology is currently widely used due to its benefits on high resource utilization, flexible manageability and powerful system security. However, its use for high performance computing (HPC) is still not popular due to the unclearness of the virtualization overheads. It's worthy to evaluate the virtualization cost and to find the performance bottleneck when running HPC applications...
The speed of the memory subsystem often constrains the performance of large-scale parallel applications. Experts tune such applications to use hierarchical memory subsystems efficiently. Hardware accelerators, such as GPUs, can potentially improve memory performance beyond the capabilities of traditional hierarchical systems. However, the addition of such specialized hardware complicates code porting...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.