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J.-l. Vay - One of the best experts on this subject based on the ideXlab platform.
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an efficient and portable simd algorithm for charge current deposition in particle in cell codes
Computer Physics Communications, 2017Co-Authors: H. Vincenti, M. Lobet, R. Lehe, R. Sasanka, J.-l. VayAbstract:Abstract In current computer architectures , Data movement (from die to network) is by far the most energy consuming part of an algorithm ( ≈ 20 pJ/word on-die to ≈ 10,000 pJ/word on the network). To increase memory locality at the hardware level and reduce energy consumption related to Data movement, future exascale computers tend to use many-core processors on each compute nodes that will have a reduced clock speed to allow for efficient cooling. To compensate for frequency decrease, machine vendors are making use of long SIMD instruction Registers that are able to process multiple Data with one arithmetic operator in one clock cycle. SIMD Register length is expected to double every four years. As a consequence, Particle-In-Cell (PIC) codes will have to achieve good vectorization to fully take advantage of these upcoming architectures. In this paper, we present a new algorithm that allows for efficient and portable SIMD vectorization of current/charge deposition routines that are, along with the field gathering routines, among the most time consuming parts of the PIC algorithm. Our new algorithm uses a particular Data structure that takes into account memory alignment constraints and avoids gather/scatter instructions that can significantly affect vectorization performances on current CPUs. The new algorithm was successfully implemented in the 3D skeleton PIC code PICSAR and tested on Haswell Xeon processors (AVX2-256 bits wide Data Registers). Results show a factor of × 2 to × 2.5 speed-up in double precision for particle shape factor of orders 1 – 3 . The new algorithm can be applied as is on future KNL (Knights Landing) architectures that will include AVX-512 instruction sets with 512 bits Register lengths (8 doubles/16 singles). Program summary Program Title: vec_deposition Program Files doi: http://dx.doi.org/10.17632/nh77fv9k8c.1 Licensing provisions: BSD 3-Clause Programming language: Fortran 90 External routines/libraries: OpenMP>4.0 Nature of problem: Exascale architectures will have many-core processors per node with long vector Data Registers capable of performing one single instruction on multiple Data during one clock cycle. Data Register lengths are expected to double every four years and this pushes for new portable solutions for efficiently vectorizing Particle-In-Cell codes on these future many-core architectures. One of the main hotspot routines of the PIC algorithm is the current/charge deposition for which there is no efficient and portable vector algorithm. Solution method: Here we provide an efficient and portable vector algorithm of current/charge deposition routines that uses a new Data structure, which significantly reduces gather/scatter operations. Vectorization is controlled using OpenMP 4.0 compiler directives for vectorization which ensures portability across different architectures. Restrictions: Here we do not provide the full PIC algorithm with an executable but only vector routines for current/charge deposition. These scalar/vector routines can be used as library routines in your 3D Particle-In-Cell code. However, to get the best performances out of vector routines you have to satisfy the two following requirements: (1) Your code should implement particle tiling (as explained in the manuscript) to allow for maximized cache reuse and reduce memory accesses that can hinder vector performances. The routines can be used directly on each particle tile. (2) You should compile your code with a Fortran 90 compiler (e.g Intel, gnu or cray) and provide proper alignment flags and compiler alignment directives (more details in README file).
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An efficient and portable SIMD algorithm for charge/current deposition in Particle-In-Cell codes
Computer Physics Communications, 2017Co-Authors: H. Vincenti, M. Lobet, R. Lehe, R. Sasanka, J.-l. VayAbstract:Abstract In current computer architectures , Data movement (from die to network) is by far the most energy consuming part of an algorithm ( ≈ 20 pJ/word on-die to ≈ 10,000 pJ/word on the network). To increase memory locality at the hardware level and reduce energy consumption related to Data movement, future exascale computers tend to use many-core processors on each compute nodes that will have a reduced clock speed to allow for efficient cooling. To compensate for frequency decrease, machine vendors are making use of long SIMD instruction Registers that are able to process multiple Data with one arithmetic operator in one clock cycle. SIMD Register length is expected to double every four years. As a consequence, Particle-In-Cell (PIC) codes will have to achieve good vectorization to fully take advantage of these upcoming architectures. In this paper, we present a new algorithm that allows for efficient and portable SIMD vectorization of current/charge deposition routines that are, along with the field gathering routines, among the most time consuming parts of the PIC algorithm. Our new algorithm uses a particular Data structure that takes into account memory alignment constraints and avoids gather/scatter instructions that can significantly affect vectorization performances on current CPUs. The new algorithm was successfully implemented in the 3D skeleton PIC code PICSAR and tested on Haswell Xeon processors (AVX2-256 bits wide Data Registers). Results show a factor of × 2 to × 2.5 speed-up in double precision for particle shape factor of orders 1 – 3 . The new algorithm can be applied as is on future KNL (Knights Landing) architectures that will include AVX-512 instruction sets with 512 bits Register lengths (8 doubles/16 singles). Program summary Program Title: vec_deposition Program Files doi: http://dx.doi.org/10.17632/nh77fv9k8c.1 Licensing provisions: BSD 3-Clause Programming language: Fortran 90 External routines/libraries: OpenMP>4.0 Nature of problem: Exascale architectures will have many-core processors per node with long vector Data Registers capable of performing one single instruction on multiple Data during one clock cycle. Data Register lengths are expected to double every four years and this pushes for new portable solutions for efficiently vectorizing Particle-In-Cell codes on these future many-core architectures. One of the main hotspot routines of the PIC algorithm is the current/charge deposition for which there is no efficient and portable vector algorithm. Solution method: Here we provide an efficient and portable vector algorithm of current/charge deposition routines that uses a new Data structure, which significantly reduces gather/scatter operations. Vectorization is controlled using OpenMP 4.0 compiler directives for vectorization which ensures portability across different architectures. Restrictions: Here we do not provide the full PIC algorithm with an executable but only vector routines for current/charge deposition. These scalar/vector routines can be used as library routines in your 3D Particle-In-Cell code. However, to get the best performances out of vector routines you have to satisfy the two following requirements: (1) Your code should implement particle tiling (as explained in the manuscript) to allow for maximized cache reuse and reduce memory accesses that can hinder vector performances. The routines can be used directly on each particle tile. (2) You should compile your code with a Fortran 90 compiler (e.g Intel, gnu or cray) and provide proper alignment flags and compiler alignment directives (more details in README file).
H. Vincenti - One of the best experts on this subject based on the ideXlab platform.
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an efficient and portable simd algorithm for charge current deposition in particle in cell codes
Computer Physics Communications, 2017Co-Authors: H. Vincenti, M. Lobet, R. Lehe, R. Sasanka, J.-l. VayAbstract:Abstract In current computer architectures , Data movement (from die to network) is by far the most energy consuming part of an algorithm ( ≈ 20 pJ/word on-die to ≈ 10,000 pJ/word on the network). To increase memory locality at the hardware level and reduce energy consumption related to Data movement, future exascale computers tend to use many-core processors on each compute nodes that will have a reduced clock speed to allow for efficient cooling. To compensate for frequency decrease, machine vendors are making use of long SIMD instruction Registers that are able to process multiple Data with one arithmetic operator in one clock cycle. SIMD Register length is expected to double every four years. As a consequence, Particle-In-Cell (PIC) codes will have to achieve good vectorization to fully take advantage of these upcoming architectures. In this paper, we present a new algorithm that allows for efficient and portable SIMD vectorization of current/charge deposition routines that are, along with the field gathering routines, among the most time consuming parts of the PIC algorithm. Our new algorithm uses a particular Data structure that takes into account memory alignment constraints and avoids gather/scatter instructions that can significantly affect vectorization performances on current CPUs. The new algorithm was successfully implemented in the 3D skeleton PIC code PICSAR and tested on Haswell Xeon processors (AVX2-256 bits wide Data Registers). Results show a factor of × 2 to × 2.5 speed-up in double precision for particle shape factor of orders 1 – 3 . The new algorithm can be applied as is on future KNL (Knights Landing) architectures that will include AVX-512 instruction sets with 512 bits Register lengths (8 doubles/16 singles). Program summary Program Title: vec_deposition Program Files doi: http://dx.doi.org/10.17632/nh77fv9k8c.1 Licensing provisions: BSD 3-Clause Programming language: Fortran 90 External routines/libraries: OpenMP>4.0 Nature of problem: Exascale architectures will have many-core processors per node with long vector Data Registers capable of performing one single instruction on multiple Data during one clock cycle. Data Register lengths are expected to double every four years and this pushes for new portable solutions for efficiently vectorizing Particle-In-Cell codes on these future many-core architectures. One of the main hotspot routines of the PIC algorithm is the current/charge deposition for which there is no efficient and portable vector algorithm. Solution method: Here we provide an efficient and portable vector algorithm of current/charge deposition routines that uses a new Data structure, which significantly reduces gather/scatter operations. Vectorization is controlled using OpenMP 4.0 compiler directives for vectorization which ensures portability across different architectures. Restrictions: Here we do not provide the full PIC algorithm with an executable but only vector routines for current/charge deposition. These scalar/vector routines can be used as library routines in your 3D Particle-In-Cell code. However, to get the best performances out of vector routines you have to satisfy the two following requirements: (1) Your code should implement particle tiling (as explained in the manuscript) to allow for maximized cache reuse and reduce memory accesses that can hinder vector performances. The routines can be used directly on each particle tile. (2) You should compile your code with a Fortran 90 compiler (e.g Intel, gnu or cray) and provide proper alignment flags and compiler alignment directives (more details in README file).
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An efficient and portable SIMD algorithm for charge/current deposition in Particle-In-Cell codes
Computer Physics Communications, 2017Co-Authors: H. Vincenti, M. Lobet, R. Lehe, R. Sasanka, J.-l. VayAbstract:Abstract In current computer architectures , Data movement (from die to network) is by far the most energy consuming part of an algorithm ( ≈ 20 pJ/word on-die to ≈ 10,000 pJ/word on the network). To increase memory locality at the hardware level and reduce energy consumption related to Data movement, future exascale computers tend to use many-core processors on each compute nodes that will have a reduced clock speed to allow for efficient cooling. To compensate for frequency decrease, machine vendors are making use of long SIMD instruction Registers that are able to process multiple Data with one arithmetic operator in one clock cycle. SIMD Register length is expected to double every four years. As a consequence, Particle-In-Cell (PIC) codes will have to achieve good vectorization to fully take advantage of these upcoming architectures. In this paper, we present a new algorithm that allows for efficient and portable SIMD vectorization of current/charge deposition routines that are, along with the field gathering routines, among the most time consuming parts of the PIC algorithm. Our new algorithm uses a particular Data structure that takes into account memory alignment constraints and avoids gather/scatter instructions that can significantly affect vectorization performances on current CPUs. The new algorithm was successfully implemented in the 3D skeleton PIC code PICSAR and tested on Haswell Xeon processors (AVX2-256 bits wide Data Registers). Results show a factor of × 2 to × 2.5 speed-up in double precision for particle shape factor of orders 1 – 3 . The new algorithm can be applied as is on future KNL (Knights Landing) architectures that will include AVX-512 instruction sets with 512 bits Register lengths (8 doubles/16 singles). Program summary Program Title: vec_deposition Program Files doi: http://dx.doi.org/10.17632/nh77fv9k8c.1 Licensing provisions: BSD 3-Clause Programming language: Fortran 90 External routines/libraries: OpenMP>4.0 Nature of problem: Exascale architectures will have many-core processors per node with long vector Data Registers capable of performing one single instruction on multiple Data during one clock cycle. Data Register lengths are expected to double every four years and this pushes for new portable solutions for efficiently vectorizing Particle-In-Cell codes on these future many-core architectures. One of the main hotspot routines of the PIC algorithm is the current/charge deposition for which there is no efficient and portable vector algorithm. Solution method: Here we provide an efficient and portable vector algorithm of current/charge deposition routines that uses a new Data structure, which significantly reduces gather/scatter operations. Vectorization is controlled using OpenMP 4.0 compiler directives for vectorization which ensures portability across different architectures. Restrictions: Here we do not provide the full PIC algorithm with an executable but only vector routines for current/charge deposition. These scalar/vector routines can be used as library routines in your 3D Particle-In-Cell code. However, to get the best performances out of vector routines you have to satisfy the two following requirements: (1) Your code should implement particle tiling (as explained in the manuscript) to allow for maximized cache reuse and reduce memory accesses that can hinder vector performances. The routines can be used directly on each particle tile. (2) You should compile your code with a Fortran 90 compiler (e.g Intel, gnu or cray) and provide proper alignment flags and compiler alignment directives (more details in README file).
R. Sasanka - One of the best experts on this subject based on the ideXlab platform.
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an efficient and portable simd algorithm for charge current deposition in particle in cell codes
Computer Physics Communications, 2017Co-Authors: H. Vincenti, M. Lobet, R. Lehe, R. Sasanka, J.-l. VayAbstract:Abstract In current computer architectures , Data movement (from die to network) is by far the most energy consuming part of an algorithm ( ≈ 20 pJ/word on-die to ≈ 10,000 pJ/word on the network). To increase memory locality at the hardware level and reduce energy consumption related to Data movement, future exascale computers tend to use many-core processors on each compute nodes that will have a reduced clock speed to allow for efficient cooling. To compensate for frequency decrease, machine vendors are making use of long SIMD instruction Registers that are able to process multiple Data with one arithmetic operator in one clock cycle. SIMD Register length is expected to double every four years. As a consequence, Particle-In-Cell (PIC) codes will have to achieve good vectorization to fully take advantage of these upcoming architectures. In this paper, we present a new algorithm that allows for efficient and portable SIMD vectorization of current/charge deposition routines that are, along with the field gathering routines, among the most time consuming parts of the PIC algorithm. Our new algorithm uses a particular Data structure that takes into account memory alignment constraints and avoids gather/scatter instructions that can significantly affect vectorization performances on current CPUs. The new algorithm was successfully implemented in the 3D skeleton PIC code PICSAR and tested on Haswell Xeon processors (AVX2-256 bits wide Data Registers). Results show a factor of × 2 to × 2.5 speed-up in double precision for particle shape factor of orders 1 – 3 . The new algorithm can be applied as is on future KNL (Knights Landing) architectures that will include AVX-512 instruction sets with 512 bits Register lengths (8 doubles/16 singles). Program summary Program Title: vec_deposition Program Files doi: http://dx.doi.org/10.17632/nh77fv9k8c.1 Licensing provisions: BSD 3-Clause Programming language: Fortran 90 External routines/libraries: OpenMP>4.0 Nature of problem: Exascale architectures will have many-core processors per node with long vector Data Registers capable of performing one single instruction on multiple Data during one clock cycle. Data Register lengths are expected to double every four years and this pushes for new portable solutions for efficiently vectorizing Particle-In-Cell codes on these future many-core architectures. One of the main hotspot routines of the PIC algorithm is the current/charge deposition for which there is no efficient and portable vector algorithm. Solution method: Here we provide an efficient and portable vector algorithm of current/charge deposition routines that uses a new Data structure, which significantly reduces gather/scatter operations. Vectorization is controlled using OpenMP 4.0 compiler directives for vectorization which ensures portability across different architectures. Restrictions: Here we do not provide the full PIC algorithm with an executable but only vector routines for current/charge deposition. These scalar/vector routines can be used as library routines in your 3D Particle-In-Cell code. However, to get the best performances out of vector routines you have to satisfy the two following requirements: (1) Your code should implement particle tiling (as explained in the manuscript) to allow for maximized cache reuse and reduce memory accesses that can hinder vector performances. The routines can be used directly on each particle tile. (2) You should compile your code with a Fortran 90 compiler (e.g Intel, gnu or cray) and provide proper alignment flags and compiler alignment directives (more details in README file).
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An efficient and portable SIMD algorithm for charge/current deposition in Particle-In-Cell codes
Computer Physics Communications, 2017Co-Authors: H. Vincenti, M. Lobet, R. Lehe, R. Sasanka, J.-l. VayAbstract:Abstract In current computer architectures , Data movement (from die to network) is by far the most energy consuming part of an algorithm ( ≈ 20 pJ/word on-die to ≈ 10,000 pJ/word on the network). To increase memory locality at the hardware level and reduce energy consumption related to Data movement, future exascale computers tend to use many-core processors on each compute nodes that will have a reduced clock speed to allow for efficient cooling. To compensate for frequency decrease, machine vendors are making use of long SIMD instruction Registers that are able to process multiple Data with one arithmetic operator in one clock cycle. SIMD Register length is expected to double every four years. As a consequence, Particle-In-Cell (PIC) codes will have to achieve good vectorization to fully take advantage of these upcoming architectures. In this paper, we present a new algorithm that allows for efficient and portable SIMD vectorization of current/charge deposition routines that are, along with the field gathering routines, among the most time consuming parts of the PIC algorithm. Our new algorithm uses a particular Data structure that takes into account memory alignment constraints and avoids gather/scatter instructions that can significantly affect vectorization performances on current CPUs. The new algorithm was successfully implemented in the 3D skeleton PIC code PICSAR and tested on Haswell Xeon processors (AVX2-256 bits wide Data Registers). Results show a factor of × 2 to × 2.5 speed-up in double precision for particle shape factor of orders 1 – 3 . The new algorithm can be applied as is on future KNL (Knights Landing) architectures that will include AVX-512 instruction sets with 512 bits Register lengths (8 doubles/16 singles). Program summary Program Title: vec_deposition Program Files doi: http://dx.doi.org/10.17632/nh77fv9k8c.1 Licensing provisions: BSD 3-Clause Programming language: Fortran 90 External routines/libraries: OpenMP>4.0 Nature of problem: Exascale architectures will have many-core processors per node with long vector Data Registers capable of performing one single instruction on multiple Data during one clock cycle. Data Register lengths are expected to double every four years and this pushes for new portable solutions for efficiently vectorizing Particle-In-Cell codes on these future many-core architectures. One of the main hotspot routines of the PIC algorithm is the current/charge deposition for which there is no efficient and portable vector algorithm. Solution method: Here we provide an efficient and portable vector algorithm of current/charge deposition routines that uses a new Data structure, which significantly reduces gather/scatter operations. Vectorization is controlled using OpenMP 4.0 compiler directives for vectorization which ensures portability across different architectures. Restrictions: Here we do not provide the full PIC algorithm with an executable but only vector routines for current/charge deposition. These scalar/vector routines can be used as library routines in your 3D Particle-In-Cell code. However, to get the best performances out of vector routines you have to satisfy the two following requirements: (1) Your code should implement particle tiling (as explained in the manuscript) to allow for maximized cache reuse and reduce memory accesses that can hinder vector performances. The routines can be used directly on each particle tile. (2) You should compile your code with a Fortran 90 compiler (e.g Intel, gnu or cray) and provide proper alignment flags and compiler alignment directives (more details in README file).
R. Lehe - One of the best experts on this subject based on the ideXlab platform.
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an efficient and portable simd algorithm for charge current deposition in particle in cell codes
Computer Physics Communications, 2017Co-Authors: H. Vincenti, M. Lobet, R. Lehe, R. Sasanka, J.-l. VayAbstract:Abstract In current computer architectures , Data movement (from die to network) is by far the most energy consuming part of an algorithm ( ≈ 20 pJ/word on-die to ≈ 10,000 pJ/word on the network). To increase memory locality at the hardware level and reduce energy consumption related to Data movement, future exascale computers tend to use many-core processors on each compute nodes that will have a reduced clock speed to allow for efficient cooling. To compensate for frequency decrease, machine vendors are making use of long SIMD instruction Registers that are able to process multiple Data with one arithmetic operator in one clock cycle. SIMD Register length is expected to double every four years. As a consequence, Particle-In-Cell (PIC) codes will have to achieve good vectorization to fully take advantage of these upcoming architectures. In this paper, we present a new algorithm that allows for efficient and portable SIMD vectorization of current/charge deposition routines that are, along with the field gathering routines, among the most time consuming parts of the PIC algorithm. Our new algorithm uses a particular Data structure that takes into account memory alignment constraints and avoids gather/scatter instructions that can significantly affect vectorization performances on current CPUs. The new algorithm was successfully implemented in the 3D skeleton PIC code PICSAR and tested on Haswell Xeon processors (AVX2-256 bits wide Data Registers). Results show a factor of × 2 to × 2.5 speed-up in double precision for particle shape factor of orders 1 – 3 . The new algorithm can be applied as is on future KNL (Knights Landing) architectures that will include AVX-512 instruction sets with 512 bits Register lengths (8 doubles/16 singles). Program summary Program Title: vec_deposition Program Files doi: http://dx.doi.org/10.17632/nh77fv9k8c.1 Licensing provisions: BSD 3-Clause Programming language: Fortran 90 External routines/libraries: OpenMP>4.0 Nature of problem: Exascale architectures will have many-core processors per node with long vector Data Registers capable of performing one single instruction on multiple Data during one clock cycle. Data Register lengths are expected to double every four years and this pushes for new portable solutions for efficiently vectorizing Particle-In-Cell codes on these future many-core architectures. One of the main hotspot routines of the PIC algorithm is the current/charge deposition for which there is no efficient and portable vector algorithm. Solution method: Here we provide an efficient and portable vector algorithm of current/charge deposition routines that uses a new Data structure, which significantly reduces gather/scatter operations. Vectorization is controlled using OpenMP 4.0 compiler directives for vectorization which ensures portability across different architectures. Restrictions: Here we do not provide the full PIC algorithm with an executable but only vector routines for current/charge deposition. These scalar/vector routines can be used as library routines in your 3D Particle-In-Cell code. However, to get the best performances out of vector routines you have to satisfy the two following requirements: (1) Your code should implement particle tiling (as explained in the manuscript) to allow for maximized cache reuse and reduce memory accesses that can hinder vector performances. The routines can be used directly on each particle tile. (2) You should compile your code with a Fortran 90 compiler (e.g Intel, gnu or cray) and provide proper alignment flags and compiler alignment directives (more details in README file).
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An efficient and portable SIMD algorithm for charge/current deposition in Particle-In-Cell codes
Computer Physics Communications, 2017Co-Authors: H. Vincenti, M. Lobet, R. Lehe, R. Sasanka, J.-l. VayAbstract:Abstract In current computer architectures , Data movement (from die to network) is by far the most energy consuming part of an algorithm ( ≈ 20 pJ/word on-die to ≈ 10,000 pJ/word on the network). To increase memory locality at the hardware level and reduce energy consumption related to Data movement, future exascale computers tend to use many-core processors on each compute nodes that will have a reduced clock speed to allow for efficient cooling. To compensate for frequency decrease, machine vendors are making use of long SIMD instruction Registers that are able to process multiple Data with one arithmetic operator in one clock cycle. SIMD Register length is expected to double every four years. As a consequence, Particle-In-Cell (PIC) codes will have to achieve good vectorization to fully take advantage of these upcoming architectures. In this paper, we present a new algorithm that allows for efficient and portable SIMD vectorization of current/charge deposition routines that are, along with the field gathering routines, among the most time consuming parts of the PIC algorithm. Our new algorithm uses a particular Data structure that takes into account memory alignment constraints and avoids gather/scatter instructions that can significantly affect vectorization performances on current CPUs. The new algorithm was successfully implemented in the 3D skeleton PIC code PICSAR and tested on Haswell Xeon processors (AVX2-256 bits wide Data Registers). Results show a factor of × 2 to × 2.5 speed-up in double precision for particle shape factor of orders 1 – 3 . The new algorithm can be applied as is on future KNL (Knights Landing) architectures that will include AVX-512 instruction sets with 512 bits Register lengths (8 doubles/16 singles). Program summary Program Title: vec_deposition Program Files doi: http://dx.doi.org/10.17632/nh77fv9k8c.1 Licensing provisions: BSD 3-Clause Programming language: Fortran 90 External routines/libraries: OpenMP>4.0 Nature of problem: Exascale architectures will have many-core processors per node with long vector Data Registers capable of performing one single instruction on multiple Data during one clock cycle. Data Register lengths are expected to double every four years and this pushes for new portable solutions for efficiently vectorizing Particle-In-Cell codes on these future many-core architectures. One of the main hotspot routines of the PIC algorithm is the current/charge deposition for which there is no efficient and portable vector algorithm. Solution method: Here we provide an efficient and portable vector algorithm of current/charge deposition routines that uses a new Data structure, which significantly reduces gather/scatter operations. Vectorization is controlled using OpenMP 4.0 compiler directives for vectorization which ensures portability across different architectures. Restrictions: Here we do not provide the full PIC algorithm with an executable but only vector routines for current/charge deposition. These scalar/vector routines can be used as library routines in your 3D Particle-In-Cell code. However, to get the best performances out of vector routines you have to satisfy the two following requirements: (1) Your code should implement particle tiling (as explained in the manuscript) to allow for maximized cache reuse and reduce memory accesses that can hinder vector performances. The routines can be used directly on each particle tile. (2) You should compile your code with a Fortran 90 compiler (e.g Intel, gnu or cray) and provide proper alignment flags and compiler alignment directives (more details in README file).
M. Lobet - One of the best experts on this subject based on the ideXlab platform.
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an efficient and portable simd algorithm for charge current deposition in particle in cell codes
Computer Physics Communications, 2017Co-Authors: H. Vincenti, M. Lobet, R. Lehe, R. Sasanka, J.-l. VayAbstract:Abstract In current computer architectures , Data movement (from die to network) is by far the most energy consuming part of an algorithm ( ≈ 20 pJ/word on-die to ≈ 10,000 pJ/word on the network). To increase memory locality at the hardware level and reduce energy consumption related to Data movement, future exascale computers tend to use many-core processors on each compute nodes that will have a reduced clock speed to allow for efficient cooling. To compensate for frequency decrease, machine vendors are making use of long SIMD instruction Registers that are able to process multiple Data with one arithmetic operator in one clock cycle. SIMD Register length is expected to double every four years. As a consequence, Particle-In-Cell (PIC) codes will have to achieve good vectorization to fully take advantage of these upcoming architectures. In this paper, we present a new algorithm that allows for efficient and portable SIMD vectorization of current/charge deposition routines that are, along with the field gathering routines, among the most time consuming parts of the PIC algorithm. Our new algorithm uses a particular Data structure that takes into account memory alignment constraints and avoids gather/scatter instructions that can significantly affect vectorization performances on current CPUs. The new algorithm was successfully implemented in the 3D skeleton PIC code PICSAR and tested on Haswell Xeon processors (AVX2-256 bits wide Data Registers). Results show a factor of × 2 to × 2.5 speed-up in double precision for particle shape factor of orders 1 – 3 . The new algorithm can be applied as is on future KNL (Knights Landing) architectures that will include AVX-512 instruction sets with 512 bits Register lengths (8 doubles/16 singles). Program summary Program Title: vec_deposition Program Files doi: http://dx.doi.org/10.17632/nh77fv9k8c.1 Licensing provisions: BSD 3-Clause Programming language: Fortran 90 External routines/libraries: OpenMP>4.0 Nature of problem: Exascale architectures will have many-core processors per node with long vector Data Registers capable of performing one single instruction on multiple Data during one clock cycle. Data Register lengths are expected to double every four years and this pushes for new portable solutions for efficiently vectorizing Particle-In-Cell codes on these future many-core architectures. One of the main hotspot routines of the PIC algorithm is the current/charge deposition for which there is no efficient and portable vector algorithm. Solution method: Here we provide an efficient and portable vector algorithm of current/charge deposition routines that uses a new Data structure, which significantly reduces gather/scatter operations. Vectorization is controlled using OpenMP 4.0 compiler directives for vectorization which ensures portability across different architectures. Restrictions: Here we do not provide the full PIC algorithm with an executable but only vector routines for current/charge deposition. These scalar/vector routines can be used as library routines in your 3D Particle-In-Cell code. However, to get the best performances out of vector routines you have to satisfy the two following requirements: (1) Your code should implement particle tiling (as explained in the manuscript) to allow for maximized cache reuse and reduce memory accesses that can hinder vector performances. The routines can be used directly on each particle tile. (2) You should compile your code with a Fortran 90 compiler (e.g Intel, gnu or cray) and provide proper alignment flags and compiler alignment directives (more details in README file).
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An efficient and portable SIMD algorithm for charge/current deposition in Particle-In-Cell codes
Computer Physics Communications, 2017Co-Authors: H. Vincenti, M. Lobet, R. Lehe, R. Sasanka, J.-l. VayAbstract:Abstract In current computer architectures , Data movement (from die to network) is by far the most energy consuming part of an algorithm ( ≈ 20 pJ/word on-die to ≈ 10,000 pJ/word on the network). To increase memory locality at the hardware level and reduce energy consumption related to Data movement, future exascale computers tend to use many-core processors on each compute nodes that will have a reduced clock speed to allow for efficient cooling. To compensate for frequency decrease, machine vendors are making use of long SIMD instruction Registers that are able to process multiple Data with one arithmetic operator in one clock cycle. SIMD Register length is expected to double every four years. As a consequence, Particle-In-Cell (PIC) codes will have to achieve good vectorization to fully take advantage of these upcoming architectures. In this paper, we present a new algorithm that allows for efficient and portable SIMD vectorization of current/charge deposition routines that are, along with the field gathering routines, among the most time consuming parts of the PIC algorithm. Our new algorithm uses a particular Data structure that takes into account memory alignment constraints and avoids gather/scatter instructions that can significantly affect vectorization performances on current CPUs. The new algorithm was successfully implemented in the 3D skeleton PIC code PICSAR and tested on Haswell Xeon processors (AVX2-256 bits wide Data Registers). Results show a factor of × 2 to × 2.5 speed-up in double precision for particle shape factor of orders 1 – 3 . The new algorithm can be applied as is on future KNL (Knights Landing) architectures that will include AVX-512 instruction sets with 512 bits Register lengths (8 doubles/16 singles). Program summary Program Title: vec_deposition Program Files doi: http://dx.doi.org/10.17632/nh77fv9k8c.1 Licensing provisions: BSD 3-Clause Programming language: Fortran 90 External routines/libraries: OpenMP>4.0 Nature of problem: Exascale architectures will have many-core processors per node with long vector Data Registers capable of performing one single instruction on multiple Data during one clock cycle. Data Register lengths are expected to double every four years and this pushes for new portable solutions for efficiently vectorizing Particle-In-Cell codes on these future many-core architectures. One of the main hotspot routines of the PIC algorithm is the current/charge deposition for which there is no efficient and portable vector algorithm. Solution method: Here we provide an efficient and portable vector algorithm of current/charge deposition routines that uses a new Data structure, which significantly reduces gather/scatter operations. Vectorization is controlled using OpenMP 4.0 compiler directives for vectorization which ensures portability across different architectures. Restrictions: Here we do not provide the full PIC algorithm with an executable but only vector routines for current/charge deposition. These scalar/vector routines can be used as library routines in your 3D Particle-In-Cell code. However, to get the best performances out of vector routines you have to satisfy the two following requirements: (1) Your code should implement particle tiling (as explained in the manuscript) to allow for maximized cache reuse and reduce memory accesses that can hinder vector performances. The routines can be used directly on each particle tile. (2) You should compile your code with a Fortran 90 compiler (e.g Intel, gnu or cray) and provide proper alignment flags and compiler alignment directives (more details in README file).
