Leonid Djinevski

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The newest GPU Kepler architecture offers a reconfigurable L1 cache per Streaming Multiprocessor with different cache size and cache associativity. Both these cache parameters affect the overall performance of cache intensive algorithms, i.e. the algorithms which intensively reuse the data. In this paper, we analyze the impact of different configurations of(More)
Performance of shared memory processors show negative performance impulses (drawbacks) in certain regions for execution of the basic matrix multiplication algorithm. In this paper we continue with analysis of GPU memory hierarchy and corresponding cache memory organization. We give a theoretical analysis why a negative performance impulse appears for(More)
GPU devices offer great performance when dealing with algorithms that require intense computational resources. A developer can configure the L1 cache memory of the latest GPU Kepler architecture with different cache size and cache set associativity, per Streaming Mul-tiprocessors (SM). The performance of the computation intensive algorithms can be affected(More)
—In this paper we discuss the possibilities for parallel implementations of network simulators. Specifically we investigate the options for porting parts of the simulator on GPU in order to utilize its resources and obtain faster simulations. We discuss few issues which are unsuitable for the GPU architecture, and we propose a possible work around for each(More)
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