what extent we achieve scaling for different parallelization strategies: C-style programming with OpenMP, native mechanisms in modern C++, as well as through Kokkos and Sycl. Discussion An important corner distributed memory, and OpenMP (Open Multi-Processing) for shared memory. In this project we focus mostly on the shared memory aspect and use OpenMP as the performance baseline. (1) OpenMP has standard bindings ‘C-style’ imple- mentation based on simple loops and linear vectors for floating point data storage. (2) OpenMP also has bindings to C++, where it can exploit a random access iterator. This means that we reimplement

64); parallel_for_each(e, [=](index<2> idx) restrict(amp) { ptr[idx] *= 2.0f; }); Here we’re using OpenMP as an example float *h_a = { … }, d_a; cudaMalloc((void **)&d_a, size); cudaMemcpy(d_a, h_a, size 64>>>(a, b, c); cudaMemcpy(d_a, h_a, size, cudaMemcpyDeviceToHost); Examples: - OpenCL, CUDA, OpenMP, SYCL 2020 Implementation: - Data is moved to the device via explicit copy APIs Here we’re using

Chair of SYCL Heterogeneous Programming Language ● ISO C++ Directions Group past Chair ● Past CEO OpenMP ● ISOCPP.org Director, VP http://isocpp.org/wiki/faq/wg21#michael-wong ● michael@codeplay.com Application uses SYCL, Kokkos, Raja SYCL in HPC/Supercomputers CUDA/pthreads/ OpenACC/OpenCL OpenMP for C and Fortran Need Languages that allow control of these Data Issues Set Data affinity, Data

Chair of SYCL Heterogeneous Programming Language ● ISO C++ Directions Group past Chair ● Past CEO OpenMP ● ISOCPP.org Director, VP http://isocpp.org/wiki/faq/wg21#michael-wong ● michael@codeplay.com ●

collection, and optimization processes. Useful for: ● Lock-free data structures ● Parallel reductions (OpenMP) ● Optimization algorithms ● Statistics collectionProposed interface namespace std { template

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Intel OpenMP runtime libraries. This is almost always caused by one of two things: 1. The environment with NumPy has not been activated. 2. Another software vendor has installed MKL or Intel OpenMP (libiomp5md

Intel OpenMP runtime libraries. This is almost always caused by one of two things: 1. The environment with NumPy has not been activated. 2. Another software vendor has installed MKL or Intel OpenMP (libiomp5md

Intel OpenMP runtime libraries. This is almost always caused by one of two things: 1. The environment with NumPy has not been activated. 2. Another software vendor has installed MKL or Intel OpenMP (libiomp5md

Intel OpenMP runtime libraries. This is almost always caused by one of two things: 1. The environment with NumPy has not been activated. 2. Another software vendor has installed MKL or Intel OpenMP (libiomp5md

Intel OpenMP runtime libraries. This is almost always caused by one of two things: 1. The environment with NumPy has not been activated. 2. Another software vendor has installed MKL or Intel OpenMP (libiomp5md