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.
Jonathan Cohen and the NVIDIA CUDA Library Team present the latest benchmark results using the extensive numerical libraries included with CUDA 5. This webinar will cover all the data points and the significance of the new Math Library Performance Report
amgcl is a simple and generic algebraic multigrid (AMG) hierarchy builder. The constructed hierarchy may be used as a standalone solver or as a preconditioner with some iterative solver. Conjugate Gradient and Stabilized BiConjugate Gradient iterative solvers are provided. It is also possible to use generic solvers from other libraries, e.g. ViennaCL.
Intel engineers implemented the Microsoft specification of C++ AMP within OpenCL and using LLVM/Clang so that it can be used cross-platform.
VexCL is vector expression template library for OpenCL. It has been created for ease of C++ based OpenCL development. Multi-device (and multi-platform) computations are supported.
Now, your OpenFOAM simulations on GPU can be up to 3.5x faster compared to CG and DIC/DILU preconditioners on CPU and up to 1.6x faster if you run GAMG.
This release includes updated versions of the CUDA-GDB debugger, Visual Profiler, and others tools that support the Kepler architecture GPUs. Updated versions of NVIDIA’s GPU-accelerated libraries have are also provided in this release, including cuBLAS, cuSPARSE, cuFFT, and the NVIDIA Performance Primitives (NPP) library for image and signal processing.
StarPU’s GCC plug-in allows programmers to annotate C code to describe tasks and their implementations, as well as memory buffers that are passed to the tasks. Each task may have one or more implementations, such as CPU implementations or implementations written in OpenCL.
NVIDIA today released a new version of its CUDA parallel computing platform, which will make it easier for computational biologists, chemists, physicists, geophysicists, other researchers, and engineers to advance their simulations and computational work by using GPUs.