36,124 research outputs found

    Penerapan Multi-threading untuk Meningkatkan Kinerja Pengolahan Citra Digital

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    Penelitian ini menggunakan teknik multi-threading dalam pengolahan citra digital untuk menghasilkan pengolahan citra digital yang lebih cepat. Selain itu, penelitian ini memperbandingkan kecepatan akurasi antara single-threading dengan multi-threading. Hasil pengujian menggunakan teknik multi-threading memperlihatkan waktu proses semakin bertambah cepat, bila jumlah sampel bertambah banyak. Hal ini menunjukkan bahwa teknik multi-threading memiliki waktu proses yang optimal dalam pengolahan citra digital dibandingkan dengan single-threading

    R friendly multi-threading in C++

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    Calling multi-threaded C++ code from R has its perils. Since the R interpreter is single-threaded, one must not check for user interruptions or print to the R console from multiple threads. One can, however, synchronize with R from the main thread. The R package RcppThread (current version 0.5.3) contains a header only C++ library for thread safe communication with R that exploits this fact. It includes C++ classes for threads, a thread pool, and parallel loops that routinely synchronize with R. This article explains the package's functionality and gives examples of its usage. The synchronization mechanism may also apply to other threading frameworks. Benchmarks suggest that, although synchronization causes overhead, the parallel abstractions of RcppThread are competitive with other popular libraries in typical scenarios encountered in statistical computing

    Importance of Explicit Vectorization for CPU and GPU Software Performance

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    Much of the current focus in high-performance computing is on multi-threading, multi-computing, and graphics processing unit (GPU) computing. However, vectorization and non-parallel optimization techniques, which can often be employed additionally, are less frequently discussed. In this paper, we present an analysis of several optimizations done on both central processing unit (CPU) and GPU implementations of a particular computationally intensive Metropolis Monte Carlo algorithm. Explicit vectorization on the CPU and the equivalent, explicit memory coalescing, on the GPU are found to be critical to achieving good performance of this algorithm in both environments. The fully-optimized CPU version achieves a 9x to 12x speedup over the original CPU version, in addition to speedup from multi-threading. This is 2x faster than the fully-optimized GPU version.Comment: 17 pages, 17 figure

    Dynamic Management of Hardware Multi-threading for Network Processors

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    AbstractIn this paper, we presented a dynamic management mechanism of hardware multi-threading for pipelined hardware multi-threading architecture. And a set of special instructions are provided. In view of the workload and traffic of network are uncertainty, the dynamic multi-threading architecture in this paper can adaptively adjust processor performance according to the workload, and achieve the effective power savings

    Mixing multi-core CPUs and GPUs for scientific simulation software

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    Recent technological and economic developments have led to widespread availability of multi-core CPUs and specialist accelerator processors such as graphical processing units (GPUs). The accelerated computational performance possible from these devices can be very high for some applications paradigms. Software languages and systems such as NVIDIA's CUDA and Khronos consortium's open compute language (OpenCL) support a number of individual parallel application programming paradigms. To scale up the performance of some complex systems simulations, a hybrid of multi-core CPUs for coarse-grained parallelism and very many core GPUs for data parallelism is necessary. We describe our use of hybrid applica- tions using threading approaches and multi-core CPUs to control independent GPU devices. We present speed-up data and discuss multi-threading software issues for the applications level programmer and o er some suggested areas for language development and integration between coarse-grained and ne-grained multi-thread systems. We discuss results from three common simulation algorithmic areas including: partial di erential equations; graph cluster metric calculations and random number generation. We report on programming experiences and selected performance for these algorithms on: single and multiple GPUs; multi-core CPUs; a CellBE; and using OpenCL. We discuss programmer usability issues and the outlook and trends in multi-core programming for scienti c applications developers

    Personal Multi-threading

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    Multi-threading allows agents to pursue a heterogeneous collection of tasks in an orderly manner. The view of multi-threading that emerges from thread algebra is applied to the case where a single agent, who may be human, maintains a hierarchical multithread as an architecture of its own activities
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