The Experts below are selected from a list of 14193 Experts worldwide ranked by ideXlab platform
Jeremy Sweezy - One of the best experts on this subject based on the ideXlab platform.
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a monte carlo volumetric ray casting estimator for global fluence tallies on gpus
2018Co-Authors: Jeremy SweezyAbstract:Abstract A Monte Carlo fluence estimator has been designed to take advantage of the computational power of graphical processing units (GPUs). This new estimator, termed the volumetric-ray-casting estimator, is an extension of the expectation estimator. It can be used as a replacement of the track-length estimator for the estimation of global fluence. Calculations for this estimator are performed on the GPU while the Monte Carlo random walk is performed on the central processing unit (CPU). This method lowers the implementation cost for GPU acceleration of existing Monte Carlo particle transport codes as there is little modification of the particle history logic flow. Three test problems have been evaluated to assess the performance of the volumetric-ray-casting estimator for neutron transport on GPU hardware in comparison to the standard track-length estimator on CPU hardware. Evaluation of neutron transport through air in a criticality accident scenario showed that the volumetric-ray-casting estimator achieved 23 times the performance of the track-length estimator using a single core CPU paired with a GPU and 15 times the performance of the track-length estimator using an eight core CPU paired with a GPU. Simulation of a pressurized water reactor fuel assembly showed that the performance improvement was 6 times within the fuel and 7 times within the control rods using an eight core CPU paired with a single GPU.
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a monte carlo volumetric ray casting estimator for global fluence tallies on gpus
2018Co-Authors: Jeremy SweezyAbstract:Abstract A Monte Carlo fluence estimator has been designed to take advantage of the computational power of graphical processing units (GPUs). This new estimator, termed the volumetric-ray-casting estimator, is an extension of the expectation estimator. It can be used as a replacement of the track-length estimator for the estimation of global fluence. Calculations for this estimator are performed on the GPU while the Monte Carlo random walk is performed on the central processing unit (CPU). This method lowers the implementation cost for GPU acceleration of existing Monte Carlo particle transport codes as there is little modification of the particle history logic flow. Three test problems have been evaluated to assess the performance of the volumetric-ray-casting estimator for neutron transport on GPU hardware in comparison to the standard track-length estimator on CPU hardware. Evaluation of neutron transport through air in a criticality accident scenario showed that the volumetric-ray-casting estimator achieved 23 times the performance of the track-length estimator using a single core CPU paired with a GPU and 15 times the performance of the track-length estimator using an eight core CPU paired with a GPU. Simulation of a pressurized water reactor fuel assembly showed that the performance improvement was 6 times within the fuel and 7 times within the control rods using an eight core CPU paired with a single GPU.
Christophe Calvin - One of the best experts on this subject based on the ideXlab platform.
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competing energy lookup algorithms in monte carlo neutron transport calculations and their optimization on cpu and intel mic architectures
2017Co-Authors: Yunsong Wang, Emeric Brun, Fausto Malvagi, Christophe CalvinAbstract:Abstract The Monte Carlo method is a common and accurate way to model neutron transport with minimal approximations. However, such method is rather time-consuming due to its slow convergence rate. More specifically, the energy lookup process for cross sections can take up to 80% of overall computing time and therefore becomes an important performance hot-spot. Several optimization solutions have been already proposed: unionized grid, hashing and fractional cascading methods. In this paper we revisit those algorithms for both CPU and Many Integrated Core (MIC) architectures and introduce vectorized versions. Tests are performed with the PATMOS Monte Carlo prototype, and algorithms are evaluated and compared in terms of time performance and memory usage. Results show that significant speedup can be achieved over the conventional binary search on both CPU and MIC. Using vectorization instructions has been proved efficient on manycore architecture due to its 512-bit Vector Processing Unit (Vpu); on CPU this improvement is limited by the smaller Vpu width. Further optimization like memory reduction turns out to be very important since it largely improves computing performance.
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competing energy lookup algorithms in monte carlo neutron transport calculations and their optimization on cpu and intel mic architectures
2016Co-Authors: Yunsong Wang, Emeric Brun, Fausto Malvagi, Christophe CalvinAbstract:Abstract The Monte Carlo method is a common and accurate way to model neutron transport with minimal approximations. However, such method is rather time-consuming due to its slow convergence rate. More specifically, the energy lookup process for cross sections can take up to 80% of overall computing time and therefore becomes an important performance hotspot. Several optimization solutions have been already proposed: unionized grid, hashing and fractional cascading methods. In this paper we revisit those algorithms for both CPU and manycore (Intel MIC) architectures and introduce vectorized versions. Tests are performed with the PATMOS Monte Carlo prototype, and algorithms are evaluated and compared in terms of time performance and memory usage. Results show that significant speedup can be achieved over the conventional binary search on both CPU and Intel MIC. Further optimization with vectorization instructions has been proved very efficient on Intel MIC architecture due to its 512-bit Vector Processing Unit (Vpu); on CPU this improvement is limited by the smaller Vpu width.
Yunsong Wang - One of the best experts on this subject based on the ideXlab platform.
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competing energy lookup algorithms in monte carlo neutron transport calculations and their optimization on cpu and intel mic architectures
2017Co-Authors: Yunsong Wang, Emeric Brun, Fausto Malvagi, Christophe CalvinAbstract:Abstract The Monte Carlo method is a common and accurate way to model neutron transport with minimal approximations. However, such method is rather time-consuming due to its slow convergence rate. More specifically, the energy lookup process for cross sections can take up to 80% of overall computing time and therefore becomes an important performance hot-spot. Several optimization solutions have been already proposed: unionized grid, hashing and fractional cascading methods. In this paper we revisit those algorithms for both CPU and Many Integrated Core (MIC) architectures and introduce vectorized versions. Tests are performed with the PATMOS Monte Carlo prototype, and algorithms are evaluated and compared in terms of time performance and memory usage. Results show that significant speedup can be achieved over the conventional binary search on both CPU and MIC. Using vectorization instructions has been proved efficient on manycore architecture due to its 512-bit Vector Processing Unit (Vpu); on CPU this improvement is limited by the smaller Vpu width. Further optimization like memory reduction turns out to be very important since it largely improves computing performance.
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competing energy lookup algorithms in monte carlo neutron transport calculations and their optimization on cpu and intel mic architectures
2016Co-Authors: Yunsong Wang, Emeric Brun, Fausto Malvagi, Christophe CalvinAbstract:Abstract The Monte Carlo method is a common and accurate way to model neutron transport with minimal approximations. However, such method is rather time-consuming due to its slow convergence rate. More specifically, the energy lookup process for cross sections can take up to 80% of overall computing time and therefore becomes an important performance hotspot. Several optimization solutions have been already proposed: unionized grid, hashing and fractional cascading methods. In this paper we revisit those algorithms for both CPU and manycore (Intel MIC) architectures and introduce vectorized versions. Tests are performed with the PATMOS Monte Carlo prototype, and algorithms are evaluated and compared in terms of time performance and memory usage. Results show that significant speedup can be achieved over the conventional binary search on both CPU and Intel MIC. Further optimization with vectorization instructions has been proved very efficient on Intel MIC architecture due to its 512-bit Vector Processing Unit (Vpu); on CPU this improvement is limited by the smaller Vpu width.
Fausto Malvagi - One of the best experts on this subject based on the ideXlab platform.
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competing energy lookup algorithms in monte carlo neutron transport calculations and their optimization on cpu and intel mic architectures
2017Co-Authors: Yunsong Wang, Emeric Brun, Fausto Malvagi, Christophe CalvinAbstract:Abstract The Monte Carlo method is a common and accurate way to model neutron transport with minimal approximations. However, such method is rather time-consuming due to its slow convergence rate. More specifically, the energy lookup process for cross sections can take up to 80% of overall computing time and therefore becomes an important performance hot-spot. Several optimization solutions have been already proposed: unionized grid, hashing and fractional cascading methods. In this paper we revisit those algorithms for both CPU and Many Integrated Core (MIC) architectures and introduce vectorized versions. Tests are performed with the PATMOS Monte Carlo prototype, and algorithms are evaluated and compared in terms of time performance and memory usage. Results show that significant speedup can be achieved over the conventional binary search on both CPU and MIC. Using vectorization instructions has been proved efficient on manycore architecture due to its 512-bit Vector Processing Unit (Vpu); on CPU this improvement is limited by the smaller Vpu width. Further optimization like memory reduction turns out to be very important since it largely improves computing performance.
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competing energy lookup algorithms in monte carlo neutron transport calculations and their optimization on cpu and intel mic architectures
2016Co-Authors: Yunsong Wang, Emeric Brun, Fausto Malvagi, Christophe CalvinAbstract:Abstract The Monte Carlo method is a common and accurate way to model neutron transport with minimal approximations. However, such method is rather time-consuming due to its slow convergence rate. More specifically, the energy lookup process for cross sections can take up to 80% of overall computing time and therefore becomes an important performance hotspot. Several optimization solutions have been already proposed: unionized grid, hashing and fractional cascading methods. In this paper we revisit those algorithms for both CPU and manycore (Intel MIC) architectures and introduce vectorized versions. Tests are performed with the PATMOS Monte Carlo prototype, and algorithms are evaluated and compared in terms of time performance and memory usage. Results show that significant speedup can be achieved over the conventional binary search on both CPU and Intel MIC. Further optimization with vectorization instructions has been proved very efficient on Intel MIC architecture due to its 512-bit Vector Processing Unit (Vpu); on CPU this improvement is limited by the smaller Vpu width.
Emeric Brun - One of the best experts on this subject based on the ideXlab platform.
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competing energy lookup algorithms in monte carlo neutron transport calculations and their optimization on cpu and intel mic architectures
2017Co-Authors: Yunsong Wang, Emeric Brun, Fausto Malvagi, Christophe CalvinAbstract:Abstract The Monte Carlo method is a common and accurate way to model neutron transport with minimal approximations. However, such method is rather time-consuming due to its slow convergence rate. More specifically, the energy lookup process for cross sections can take up to 80% of overall computing time and therefore becomes an important performance hot-spot. Several optimization solutions have been already proposed: unionized grid, hashing and fractional cascading methods. In this paper we revisit those algorithms for both CPU and Many Integrated Core (MIC) architectures and introduce vectorized versions. Tests are performed with the PATMOS Monte Carlo prototype, and algorithms are evaluated and compared in terms of time performance and memory usage. Results show that significant speedup can be achieved over the conventional binary search on both CPU and MIC. Using vectorization instructions has been proved efficient on manycore architecture due to its 512-bit Vector Processing Unit (Vpu); on CPU this improvement is limited by the smaller Vpu width. Further optimization like memory reduction turns out to be very important since it largely improves computing performance.
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competing energy lookup algorithms in monte carlo neutron transport calculations and their optimization on cpu and intel mic architectures
2016Co-Authors: Yunsong Wang, Emeric Brun, Fausto Malvagi, Christophe CalvinAbstract:Abstract The Monte Carlo method is a common and accurate way to model neutron transport with minimal approximations. However, such method is rather time-consuming due to its slow convergence rate. More specifically, the energy lookup process for cross sections can take up to 80% of overall computing time and therefore becomes an important performance hotspot. Several optimization solutions have been already proposed: unionized grid, hashing and fractional cascading methods. In this paper we revisit those algorithms for both CPU and manycore (Intel MIC) architectures and introduce vectorized versions. Tests are performed with the PATMOS Monte Carlo prototype, and algorithms are evaluated and compared in terms of time performance and memory usage. Results show that significant speedup can be achieved over the conventional binary search on both CPU and Intel MIC. Further optimization with vectorization instructions has been proved very efficient on Intel MIC architecture due to its 512-bit Vector Processing Unit (Vpu); on CPU this improvement is limited by the smaller Vpu width.
