890 research outputs found
Simulating spin models on GPU
Over the last couple of years it has been realized that the vast
computational power of graphics processing units (GPUs) could be harvested for
purposes other than the video game industry. This power, which at least
nominally exceeds that of current CPUs by large factors, results from the
relative simplicity of the GPU architectures as compared to CPUs, combined with
a large number of parallel processing units on a single chip. To benefit from
this setup for general computing purposes, the problems at hand need to be
prepared in a way to profit from the inherent parallelism and hierarchical
structure of memory accesses. In this contribution I discuss the performance
potential for simulating spin models, such as the Ising model, on GPU as
compared to conventional simulations on CPU.Comment: 5 pages, 4 figures, elsarticl
Extending and Implementing the Self-adaptive Virtual Processor for Distributed Memory Architectures
Many-core architectures of the future are likely to have distributed memory
organizations and need fine grained concurrency management to be used
effectively. The Self-adaptive Virtual Processor (SVP) is an abstract
concurrent programming model which can provide this, but the model and its
current implementations assume a single address space shared memory. We
investigate and extend SVP to handle distributed environments, and discuss a
prototype SVP implementation which transparently supports execution on
heterogeneous distributed memory clusters over TCP/IP connections, while
retaining the original SVP programming model
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