53,490 research outputs found
A pilgrimage to gravity on GPUs
In this short review we present the developments over the last 5 decades that
have led to the use of Graphics Processing Units (GPUs) for astrophysical
simulations. Since the introduction of NVIDIA's Compute Unified Device
Architecture (CUDA) in 2007 the GPU has become a valuable tool for N-body
simulations and is so popular these days that almost all papers about high
precision N-body simulations use methods that are accelerated by GPUs. With the
GPU hardware becoming more advanced and being used for more advanced algorithms
like gravitational tree-codes we see a bright future for GPU like hardware in
computational astrophysics.Comment: To appear in: European Physical Journal "Special Topics" : "Computer
Simulations on Graphics Processing Units" . 18 pages, 8 figure
Improving the scalability of parallel N-body applications with an event driven constraint based execution model
The scalability and efficiency of graph applications are significantly
constrained by conventional systems and their supporting programming models.
Technology trends like multicore, manycore, and heterogeneous system
architectures are introducing further challenges and possibilities for emerging
application domains such as graph applications. This paper explores the space
of effective parallel execution of ephemeral graphs that are dynamically
generated using the Barnes-Hut algorithm to exemplify dynamic workloads. The
workloads are expressed using the semantics of an Exascale computing execution
model called ParalleX. For comparison, results using conventional execution
model semantics are also presented. We find improved load balancing during
runtime and automatic parallelism discovery improving efficiency using the
advanced semantics for Exascale computing.Comment: 11 figure
Sympiler: Transforming Sparse Matrix Codes by Decoupling Symbolic Analysis
Sympiler is a domain-specific code generator that optimizes sparse matrix
computations by decoupling the symbolic analysis phase from the numerical
manipulation stage in sparse codes. The computation patterns in sparse
numerical methods are guided by the input sparsity structure and the sparse
algorithm itself. In many real-world simulations, the sparsity pattern changes
little or not at all. Sympiler takes advantage of these properties to
symbolically analyze sparse codes at compile-time and to apply inspector-guided
transformations that enable applying low-level transformations to sparse codes.
As a result, the Sympiler-generated code outperforms highly-optimized matrix
factorization codes from commonly-used specialized libraries, obtaining average
speedups over Eigen and CHOLMOD of 3.8X and 1.5X respectively.Comment: 12 page
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