PCAMS-2014 Topics of Interest


Abstract :

Programming on multi-to-many core processor Systems with Devices is an essential part of any undergraduate, post-graduate, and research education to solve large scale applications in Science & Engineering, Computational Mathematics, & Information Sciences. FTP-2014 gives an opportunity to write, execute and demonstrate Numerical and Non-numeric Computations using different programming paradigms. The workshop provides an opportunity to develop expertise on programming and performance aspects on parallel processing platforms on systems with devices.

A detailed description of course contents is given below :

  • Prog. Paradigms (Pthreads, OpenMP, Intel TBB, Cilk Plus) on Shared Address Space Systems; Compiler Tech. & Vector Processing; Use of tuned Mathematical Lib.; Understand Profiling & Tools; Software Threading on Multi-Core Processors; Hands-on for Numerical & Non-Numerical Comps

  • Programming on Parallel & Distributed Shared Memory Computing Platforms: Explicit Message Passing Libraries (MPI 2.X) for Numerical & Non-NumericalComp.; Tuning & Performance on Issues on Shared Address Space Platforms; MixedProg. (MPI, OpenMP, Pthreads); Performance Issues-Application Kernels

  • Prog. on Multi-Core System with GPU Accelerators, GPU Comp. (NVIDIA); Basic Prog. on NVIDIA GPUS - CUDA 6.0; Prog. on CUDA enabled NVIDIA GPUs & CUDA SDK; CUDA Toolkit; CUDA OpenACC API; Numeral Linear Algebra on HPC GPU Cluster with CUDA enabled NVIDIA devices

  • Performance Issues on Multi-Core Systems with Devices; An Overview of OpenCLProg. (NVIDIA); Hybrid Prog.for Numerical /Non-Numerical Comps.; Use of Math. librariesonDevices; OpenCL-Tuning &Performance Issues; Micro/Macro Benchmarks; An Overview of on OpenL on AMD GPUs

  • Prog. on Intel Xeon Systems with Intel Xeon Phi Coprocessors; MPI- Intel Xeon-Phi; MPI-OpenCL; App. Kernels (PDE Solvers; Image Processing Kernels; String Search Algorithms)

  • Topics on Application Kernels :
    Application Kernels & Computational Mathematics / Information Sciences: Image Processing; Dense/ Sparse Matrix Computations; Micro/Macro Benchmarks; Solution of PDE Solvers; Case Study of one or two application kernels will be taken-up.

Laboratory Sessions :

Memory Allocators, Software Threading, Mixed Programs(OpenMP, POSIX Threads,Intel TBB, MPI, Memory allocators) for Numerical Dense/Sparse Matrix Comps.); & Non-Numerical Comps. Multi-Core Software tools; application and System Benchmarks - Top-500 & HPC Challenge Benchmarks

Programming on Intel Xeon Phi Coprocessors; MPI versus Offload; Compiler & Programming model; Prog. Paradigms - OpenMP, Intel Cilk Plus, Intel MKL; Tuning Memory Allocation Performance. Huge Page Sizes; Profiling & Tuning Tools- PAPI & MPI tools; Basicprog. (NVIDIA GPU Comp. CUDA 6.0 SDK &AMD GPGPUs - AMD-APP SDK); CUDA Toolkit; CUDA Matrix Comps. Lib,;OpenCL - NVIDIA - CUDA Prog. on Numerical Comps. CUDA Streams; Multi-GPU progs,; NVIDIA CUDA OpenACC - Prog.

AMD-APP- OpenCL; AMD-APP ACML-CAL Lib.; OpenCL/CUDA -Multi-GPU; NVIDIA /AMD-APP Profilers & Tools; NVIDIA NVML APIs - Prog. Performance for application kernels CUDA /OpenCL Programs on Numerical Comps. (Dense /Sparse Matrix Comps.); Partial Diff. Eqs.; String Search Alg.; FFTs; Image Proc Alg. Prog, Env. on HPC GPU Cluster; Performance issues of Benchmarks & Application Kernels on HPC GPU Cluster; Mixed Prog. on Host CPU (MPI, OpenMP, Pthreads) and CUDA/OpenCL on device GPUs,OpenACC, OpenMP 4.0.Programming