Tag: accelerated computing
For the third consecutive year, AMD invites computing innovators, developers and researchers in the rapidly growing field of heterogeneous computing to submit their latest work and research findings in the form of archival presentations for AMD’s annual developer summit.
PARALUTION is a library for sparse iterative methods with special focus on multi-core and accelerator technology such as GPUs. The software provides fine-grained parallel preconditioners which can utilize the modern multi-/many-core devices.
In this talk we will provide an introduction to pyOpenCL, python interface to the Open Computing Language. OpenCL is a framework to execute parallel programs across heterogeneous platforms consisting of of both CPUs and GPUs.
Speed without compromise: A mixed precision model for GPU accelerated molecular dynamics simulations
We present an implementation for NVIDIA GPUs of both generalized Born implicit solvent simulations as well as explicit solvent simulations using the particle mesh Ewald (PME) algorithm for long-range electrostatics
OpenCL is possibly the first programming language promising portability across accelerators: “OpenCL is for accelerators what C is for CPUs”. Portability is disruptive. When hardware vendor A displaces vendor B, portable software usually helps a great deal. Will OpenCL – “the GPGPU language” – eventually help displace GPGPU?
Logo: High Performance GPU-based TSP Solver. It is an approximate stochastic solver based on Iterative Local Search and 2-opt local search merhod. Despite limited shared memory resources, this implementation is able to solve arbitrarily large instances.
This course covers concepts and approaches related to programming GPU processors using OpenACC directives and the PGI Accelerator programming model.