The paper presents the implementation of a parallel version of FDK （Felkamp, David e Kress） algorithm using graphics processing units. Discussion was briefly some elements the computed tomographic scan and FDK algor...The paper presents the implementation of a parallel version of FDK （Felkamp, David e Kress） algorithm using graphics processing units. Discussion was briefly some elements the computed tomographic scan and FDK algorithm; and some ideas about GPUs （Graphics Processing Units） and its use in general purpose computing were presented. The paper shows a computational implementation of FDK algorithm and the process of parallelization of this implementation. Compare the parallel version of the algorithm with the sequential version, used speedup as a performance metric. To evaluate the performance of parallel version, two GPUs, GeForce 9400GT （16 cores） a low capacity GPU and Quadro 2000 （192 cores） a medium capacity GPU was reached speedup of 3.37.展开更多
In this paper, we study the impact of copying data in GPU computing. GPU computing allows implementing parallel computations at low cost: a GPU can be purchased at under USD 500. Many studies have shown that GPU can b...In this paper, we study the impact of copying data in GPU computing. GPU computing allows implementing parallel computations at low cost: a GPU can be purchased at under USD 500. Many studies have shown that GPU can be used to speed up the calculations. But for algorithms requiring doing a part of the calculations on GPU and another part on CPU, alternately, latency due to the copy of the data is a performance degradation factor. To illustrate this, we consider the Dijkstra’s algorithm on the shortest path used in solving optimization problems. This algorithm is very heavy to run on sequential machine. So, we are considering a parallel approach on GPU. Note that Dijkstra’s algorithm has been subject of many implementations on GPU. In the present work, we use two platforms with external GPU. Graphs are represented in adjacency matrix. During the computation of this algorithm, intermediates results are copied from GPU to CPU or from CPU to GPU. The purpose of this work is to measure the impact of these copies in the overall performance of the algorithm. For that we calculate time due to the copying data’s implementation;then we compare results with implementation computing only on CPU memory (zero-copy). The real impact shown by experiments demonstrates the interest of this study. GP-GPU programmers have to think that they will use either memory zero-copy or GPU memory. The challenge for GPU’s manufacturers is how to reduce this impact.展开更多
文摘The paper presents the implementation of a parallel version of FDK （Felkamp, David e Kress） algorithm using graphics processing units. Discussion was briefly some elements the computed tomographic scan and FDK algorithm; and some ideas about GPUs （Graphics Processing Units） and its use in general purpose computing were presented. The paper shows a computational implementation of FDK algorithm and the process of parallelization of this implementation. Compare the parallel version of the algorithm with the sequential version, used speedup as a performance metric. To evaluate the performance of parallel version, two GPUs, GeForce 9400GT （16 cores） a low capacity GPU and Quadro 2000 （192 cores） a medium capacity GPU was reached speedup of 3.37.
文摘In this paper, we study the impact of copying data in GPU computing. GPU computing allows implementing parallel computations at low cost: a GPU can be purchased at under USD 500. Many studies have shown that GPU can be used to speed up the calculations. But for algorithms requiring doing a part of the calculations on GPU and another part on CPU, alternately, latency due to the copy of the data is a performance degradation factor. To illustrate this, we consider the Dijkstra’s algorithm on the shortest path used in solving optimization problems. This algorithm is very heavy to run on sequential machine. So, we are considering a parallel approach on GPU. Note that Dijkstra’s algorithm has been subject of many implementations on GPU. In the present work, we use two platforms with external GPU. Graphs are represented in adjacency matrix. During the computation of this algorithm, intermediates results are copied from GPU to CPU or from CPU to GPU. The purpose of this work is to measure the impact of these copies in the overall performance of the algorithm. For that we calculate time due to the copying data’s implementation;then we compare results with implementation computing only on CPU memory (zero-copy). The real impact shown by experiments demonstrates the interest of this study. GP-GPU programmers have to think that they will use either memory zero-copy or GPU memory. The challenge for GPU’s manufacturers is how to reduce this impact.
