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hyPACK-2013 : Mode-2 (ARM Coprocessor ) Laboratory : Topics

Topics dealing with all practical and experimental aspects of various programming paradigms are considered and example programs are made available to the participants in the laboratory session. The hyPACK-2013 Mode-2 programme covers an overview of ARM microprocessor technology which address the performance, power and cost requirements for almost all applications. ARM development platform featuring NVIDIA Tegra processors are being used in HPC. ARM platforms with CUDA parallel programming toolkit, provides the foundation for developers to build out the ARM HPC application ecosystem. The CARMA DevKit features the NVIDIA Tegra 3 Quad-core ARM A9 CPU and the NVIDIA Quadro 1000M GPU with 96 CUDA cores. It offers HPC developers a simple way to create CUDA applications for GPU-accelerated systems with ARM processors. The topics such as Tuning and Performance Issues, Power Consumption for Application Kernels, Measurement of Power Consumption - using External Power-Off-Meter, and Programming on ARM processor multi-core processor systems will be discussed.

Participants will get an opportunity to walk-through and execute some of the programs designed for Mode-2 of this workshop. To understand scalability and performance of selective scientific and engineering or commercial applications, minor or substantive modification of the hyPACK-2013 software programs may be required. Efforts are on to include State-of-the-Art Multi-Core Coprocessor ARM Systems as well as NVIDIA CUDA - carma DevKit - GPU based acclerator Servers in hyPACK-2013 laboratory in order to understand measurement of power consumption and performance issues for large scale application kernels.

Mode-2 Performance - ARM Multi-Core Processors

  • ARM Processors - An Overview of Architecture & programming environment
  • Software Multi-threading & System Overview of threading
  • Parallel Processing - An Overview of Programming (POSIX Threads, Intel TBB, OpenMP)
  • Tuning & Performance of Application kernels using NVIDIA carma DevKIt
  • Performance Issues of OpenMP 3.X & Pthread Programming on ARM Processors
  • Measure Power Consumption and Performance of Benchmarks using CUDA enabled NVIDIA GPUs - carma DevKit.
Laboratory Session Mode-1 (Two days)
  • Programming exercises for Numerical and Non-Numerical Computations based on MPI, Pthreads, OpenMP, Java Concurrent APIs, & Mixed programming
  • Numerical Computations (Dense Matrix Computations, Sparse Matrix Computations), Non-Numerical Computations (Sorting & Search algorithms)
  • Tuning & Performance - Selective Application Kernels & System Benchmarks on ARM Processors
Centre for Development of Advanced Computing