Centre for Development of Advanced Computing (C-DAC) Pune, and CUDA Centre of Excellence (CCOE) of Indian Institute of Technology, Bombay are jointly conducting five-day workshop on "Parallel Computing Architecture and Applications on Multi Core to Many Core Processing Systems" (PCAMS-2014) during June 16-20, 2014 at Lecture Hall Complex, IIT Bombay.
Click here to download PCAMS-2014 poster


"Parallel Computing Architecture and Applications on Multi Core to Many Core Processing Systems" is a 5 day workshop jointly conducted by CCOE, IIT Bombay and CDAC Pune, for training faculty in the area of parallel computing. The main aim of this workshop is to stimulate teaching of parallel computing courses in Indian universities and colleges. The workshop provides a strong foundation to faculty so that they in turn can offer parallel computing courses at under-graduate and post graduate levels in their respective colleges/institutes.

The workshop covers various paradigms of parallel computing, such as shared memory programming, message passing, programming on coprocessors (Intel Xeon Phi) and accelerators (CUDA -NVIDIA). Theory and practical sessions are conducted to write, execute and demonstrate numerical and non-numeric computations using different programming paradigms. There will be several hands-on training sessions with accelerators, to give deep insights into their architecture and how-to-program for achieving optimal performances.



  • Addresses Parallel & Distributed Computing, Visual Computing, Language Computing, and Scientific & Engineering Applications

  • Gives exposure to programming on CUDA enabled NVIDIA GPU Accelerators; Offers advanced concepts on Mixed programming - Multi-Core Processors (Pthreads, OpenMP, MPI) & OpenCL on GPGPUs /GPU Computing Platforms; Programming on Intel Xeon Sysems with Intel Xeon-Phi Co Processors

  • Gives in-depth exposure to Programming on CUDA enabled NVIDIA GPUs; CUDA 6.0 SDK; & laboratory sessions; Exposure to Tuning & performance ideas on HPC Cluster with CUDA enabled NVIDIA GPUs, Cluster (CUDA/OpenCL enabled GPUs, OpenCL on AMD GPUs; Hand-on Session on Comouting systems with NVIDIA GPUs