Learn how OpenACC runtimes also exposes performance-related information revealing where your OpenACC applications are wasting clock cycles. The talk will show that profilers can connect with OpenACC applications to record how much time is spent in OpenACC regions and what device activity it turns into.
The 2nd Workshop on Accelerator Programming using Directives has issued its Call for Papers. The WACCPD Workshop takes place Nov. 16 in Austin in conjunction with SC15.
“The free ride of faster performance with increased clock speeds is long gone. Software must be both threaded and vectorized to fully utilize today’s and tomorrow’s hardware. But modernization is not without cost. Not all threading or vectorization designs are worthwhile. How do you choose which designs to implement without disrupting ongoing development? Learn how data driven threading and vectorization design can yield long term performance growth with less risk and more impact.”
“Learn how to program NVIDIA GPUs using Fortran with OpenACC directives. The first half of this presentation will introduce OpenACC to new GPU and OpenACC programmers, providing the basic material necessary to start successfully using GPUs for your Fortran programs. The second half will be intermediate material, with more advanced hints and tips for Fortran programmers with larger applications that they want to accelerate with a GPU. Among the topics to be covered will be dynamic device data lifetimes, global data, procedure calls, derived type support, and much more.”
“Based on a containerized HPC environment this talk shows of a state-of-the-art stack including performance monitoring, log event handling and GraphDB based inventory to provide insights into what is going on within a SLURM cluster. The framework used is QNIBTerminal incorporating the ELK stack, a graphite backend and neo4j as a GraphDB.”
“With the advent of massively parallel computing coprocessors, numerical optimization for deep-learning disciplines is now possible. Complex real-time pattern recognition, for example, that can be used for self driving cars and augmented reality can be developed and high performance achieved with the use of specialized, highly tuned libraries. By just using the Message Passing Interface (MPI) API, very high performance can be attained on hundreds to thousands of Intel Xeon Phi processors.”