mallocMC 2.0.0crp: Policy Based Design

<p>This release introduces mallocMC, which contains the previous algorithm and much code from ScatterAlloc 1.0.2crp. The project was renamed due to massive restructurization. The code uses ScatterAlloc as a reference algorithm, but can be extended to include other allocators in the future.</p> <p>We closed all issues documented in the Milestone <em>Get Lib ready for PIConGPU</em>.</p

mallocMC 2.1.0crp: malloc Interface, Performance, Bugs

<p>This release fixes some bugs that occured after the release of 2.0.1crp and reduces the interface to improve interoperability with the default CUDA allocator. The performance for large pages was improved.</p> <p>We closed all issues documented in Milestone <em>New Features</em>.</p

mallocMC: 2.0.1crp: Bugfixes

<p>This release fixes several bugs that occurred after the release of 2.0.0crp.</p> <p>We closed all issues documented in Milestone <em>Bugfixes</em>.</p

Supplementary Materials for "On the Scalability of Data Reduction Techniques in Current and Upcoming HPC Systems from an Application Perspective"

<p>Supplementary materials with all used benchmark scripts, plot scripts, benchmark results and PIConGPU example data for the submission to "The 1st International Workshop on Data Reduction for Big Scientific Data (DRBSD-1)" held in conjunction with ISC 2017 in Frankfurt, Germany.</p

PIConGPU, Alpaka, and cupla software bundle for IWOPH 2016 submission

<p>This is the archive containing the software used for evaluations in the publication "Performance-Portable Many-Core Plasma Simulations: Porting PIConGPU to OpenPower and Beyond" submitted to the international workshop on OpenPOWER for HPC 2016.</p> <p>The archive has the following content:</p> <p>PIConGPU Kelvin-Helmholtz Simulation code (picongpu-alpaka/):</p> <ul> <li> Remote: https://github.com/psychocoderHPC/picongpu-alpaka.git</li> <li> Branch: topic-scaling</li> <li> Commit: 1f004c8e0514ad1649f3958a6184878af6e75150</li> </ul> <p>Alpaka code (alpaka/):</p> <ul> <li>Remote: https://github.com/psychocoderHPC/alpaka.git</li> <li>Branch: topic-picongpu-alpaka</li> <li>Commit: 4a6dd35a9aff62e7f500623c3658685f827f73e5</li> </ul> <p>Cupla (cupla/):</p> <ul> <li>Remote: https://github.com/psychocoderHPC/cupla.git</li> <li>Branch: topic-dualAccelerators</li> <li>Commit: 4660f5fd8e888aa732230946046219f7e5daa1c9</li> </ul> <p>The simulation was executed for one thousand time steps and the following configuration:</p> <ul> <li>   shape is higher then CIC, we used TSC</li> <li>   pusher is Boris</li> <li>   current solver is Esirkepov (optimized, generalized)</li> <li>   Yee field solver</li> <li>   trilinear interpolation in field gathering</li> <li>   16 particles per cell</li> </ul> <p>Compile flags:</p> <ul> <li>CPU g++-4.9.2: -g0 -O3 -m64 -funroll-loops -march=native -ffast-math --param max-unroll-times=512</li> <li>GPU nvcc: --use_fast_math --ftz=false -g0 -O3 -m64</li> </ul