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Extensions to the C++ implementation of the QCD Data Parallel Interface are provided enabling acceleration of expression evaluation on NVIDIA GPUs. Single expressions are off-loaded to the device memory and execution domain leveraging the Portable Expression Template Engine and using Just-in-Time compilation techniques. Memory management is automated by a software implementation of a cache controlling the GPUs memory. Interoperability with existing Krylov space solvers is demonstrated and special attention is paid on Chroma readiness. Non-kernel routines in lattice QCD calculations typically not subject of hand-tuned optimisations are accelerated which can reduce the effects otherwise suffered from Amdahls Law.
Graphic Processing Units (GPUs) are getting increasingly important as target architectures in scientific High Performance Computing (HPC). NVIDIA established CUDA as a parallel computing architecture controlling and making use of the compute power of
Priority queue, often implemented as a heap, is an abstract data type that has been used in many well-known applications like Dijkstras shortest path algorithm, Prims minimum spanning tree, Huffman encoding, and the branch-and-bound algorithm. Howeve
Stencil computations are widely used in HPC applications. Today, many HPC platforms use GPUs as accelerators. As a result, understanding how to perform stencil computations fast on GPUs is important. While implementation strategies for low-order sten
Although the matrix multiplication plays a vital role in computational linear algebra, there are few efficient solutions for matrix multiplication of the near-sparse matrices. The Sparse Approximate Matrix Multiply (SpAMM) is one of the algorithms to
Commercial graphics processors (GPUs) have high compute capacity at very low cost, which makes them attractive for general purpose scientific computing. In this paper we show how graphics processors can be used for N-body simulations to obtain improv