24,955 research outputs found
Evaluation of Directive-Based GPU Programming Models on a Block Eigensolver with Consideration of Large Sparse Matrices
Achieving high performance and performance portability for large-scale scientific applications is a major challenge on heterogeneous computing systems such as many-core CPUs and accelerators like GPUs. In this work, we implement a widely used block eigensolver, Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG), using two popular directive based programming models (OpenMP and OpenACC) for GPU-accelerated systems. Our work differs from existing work in that it adopts a holistic approach that optimizes the full solver performance rather than narrowing the problem into small kernels (e.g., SpMM, SpMV). Our LOPBCG GPU implementation achieves a 2.8–4.3 speedup over an optimized CPU implementation when tested with four different input matrices. The evaluated configuration compared one Skylake CPU to one Skylake CPU and one NVIDIA V100 GPU. Our OpenMP and OpenACC LOBPCG GPU implementations gave nearly identical performance. We also consider how to create an efficient LOBPCG solver that can solve problems larger than GPU memory capacity. To this end, we create microbenchmarks representing the two dominant kernels (inner product and SpMM kernel) in LOBPCG and then evaluate performance when using two different programming approaches: tiling the kernels, and using Unified Memory with the original kernels. Our tiled SpMM implementation achieves a 2.9 and 48.2 speedup over the Unified Memory implementation on supercomputers with PCIe Gen3 and NVLink 2.0 CPU to GPU interconnects, respectively
A Linear Algebra Approach to Fast DNA Mixture Analysis Using GPUs
Analysis of DNA samples is an important step in forensics, and the speed of
analysis can impact investigations. Comparison of DNA sequences is based on the
analysis of short tandem repeats (STRs), which are short DNA sequences of 2-5
base pairs. Current forensics approaches use 20 STR loci for analysis. The use
of single nucleotide polymorphisms (SNPs) has utility for analysis of complex
DNA mixtures. The use of tens of thousands of SNPs loci for analysis poses
significant computational challenges because the forensic analysis scales by
the product of the loci count and number of DNA samples to be analyzed. In this
paper, we discuss the implementation of a DNA sequence comparison algorithm by
re-casting the algorithm in terms of linear algebra primitives. By developing
an overloaded matrix multiplication approach to DNA comparisons, we can
leverage advances in GPU hardware and algoithms for Dense Generalized
Matrix-Multiply (DGEMM) to speed up DNA sample comparisons. We show that it is
possible to compare 2048 unknown DNA samples with 20 million known samples in
under 6 seconds using a NVIDIA K80 GPU.Comment: Accepted for publication at the 2017 IEEE High Performance Extreme
Computing conferenc
Introducing Molly: Distributed Memory Parallelization with LLVM
Programming for distributed memory machines has always been a tedious task,
but necessary because compilers have not been sufficiently able to optimize for
such machines themselves. Molly is an extension to the LLVM compiler toolchain
that is able to distribute and reorganize workload and data if the program is
organized in statically determined loop control-flows. These are represented as
polyhedral integer-point sets that allow program transformations applied on
them. Memory distribution and layout can be declared by the programmer as
needed and the necessary asynchronous MPI communication is generated
automatically. The primary motivation is to run Lattice QCD simulations on IBM
Blue Gene/Q supercomputers, but since the implementation is not yet completed,
this paper shows the capabilities on Conway's Game of Life
GPU Accelerated Particle Visualization with Splotch
Splotch is a rendering algorithm for exploration and visual discovery in
particle-based datasets coming from astronomical observations or numerical
simulations. The strengths of the approach are production of high quality
imagery and support for very large-scale datasets through an effective mix of
the OpenMP and MPI parallel programming paradigms. This article reports our
experiences in re-designing Splotch for exploiting emerging HPC architectures
nowadays increasingly populated with GPUs. A performance model is introduced
for data transfers, computations and memory access, to guide our re-factoring
of Splotch. A number of parallelization issues are discussed, in particular
relating to race conditions and workload balancing, towards achieving optimal
performances. Our implementation was accomplished by using the CUDA programming
paradigm. Our strategy is founded on novel schemes achieving optimized data
organisation and classification of particles. We deploy a reference simulation
to present performance results on acceleration gains and scalability. We
finally outline our vision for future work developments including possibilities
for further optimisations and exploitation of emerging technologies.Comment: 25 pages, 9 figures. Astronomy and Computing (2014
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