SEMPARIS : Le serveur des séminaires parisiens

The SEMPARIS seminar webserver hosts annoucements of all seminars taking place in Paris area, in all topics of physics, mathematics and computer science. It allows registered users to receive a selection of announcements by email on a daily or weekly basis, and offers the possibility to archive PDF or Powerpoint files, making it available to the scientific community. [ More information ]

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Monday 16 September 2024, 14:15 at IPHT, Salle Claude Itzykson, Bât. 774 COURS (Cours) physics

François Gélis ( Le site de l'IPhT ) Introduction to CUDA programming for physicists

Abstract: Abstract:\newline Many numerical computations in physics require applying repetitively the same operations on large arrays, a type of task likely to receive a substantial gain in speed when parallelized. Traditionally, parallelism has been achieved by increasing the number of CPUs in a computer, or the number of computing cores in each CPU.\\ Graphical Processing Units (GPU) may be viewed as a specialized type of processor with thousands of computing cores. Thanks to a combination of hardware and software developments, their use has expanded from the domain of computer graphics into that of general purpose computing. Besides commercial softwares written to take advantage of the computing power of GPUs, several tools exist to develop computer programs that offload parts of a computation to one or more GPUs.\\ Among these programming frameworks, CUDA (Compute Unified Device Architecture) is one of the most widely employed. CUDA is an extension of the C language, but can be used also in C, FORTRAN, Python and many other programming languages. The goal of this course is to expose the concepts of CUDA programming from the ground up, in order to write simple programs that use GPUs to accelerate critical sections of code.\\ Architecture of a GPU (computing units, memory, interplay with the host computer). What type of tasks are GPUs good for? Memory operations (allocation, data transfer to and from a GPU). GPU shared memory. CUDA kernels. Streams, synchronization between dependent tasks. Reduction operations on a GPU. Available libraries: cuFFT, cuBLAS, cuSPARSE, cuRAND.

Monday 16 September 2024, 14:15 at IPHT, Salle Claude Itzykson, Bât. 774 COURS (Cours) physics

François Gélis ( Le labo ) Introduction to CUDA programming for physicists

Abstract: Abstract:\newline Many numerical computations in physics require applying repetitively the same operations on large arrays, a type of task likely to receive a substantial gain in speed when parallelized. Traditionally, parallelism has been achieved by increasing the number of CPUs in a computer, or the number of computing cores in each CPU.\\ Graphical Processing Units (GPU) may be viewed as a specialized type of processor with thousands of computing cores. Thanks to a combination of hardware and software developments, their use has expanded from the domain of computer graphics into that of general purpose computing. Besides commercial softwares written to take advantage of the computing power of GPUs, several tools exist to develop computer programs that offload parts of a computation to one or more GPUs.\\ Among these programming frameworks, CUDA (Compute Unified Device Architecture) is one of the most widely employed. CUDA is an extension of the C language, but can be used also in C, FORTRAN, Python and many other programming languages. The goal of this course is to expose the concepts of CUDA programming from the ground up, in order to write simple programs that use GPUs to accelerate critical sections of code.\\ Architecture of a GPU (computing units, memory, interplay with the host computer). What type of tasks are GPUs good for? Memory operations (allocation, data transfer to and from a GPU). GPU shared memory. CUDA kernels. Streams, synchronization between dependent tasks. Reduction operations on a GPU. Available libraries: cuFFT, cuBLAS, cuSPARSE, cuRAND.

Monday 23 September 2024, 14:15 at IPHT, Salle Claude Itzykson, Bât. 774 COURS (Cours) physics

François Gélis Introduction to CUDA programming for physicists

Abstract: Abstract:\newline Many numerical computations in physics require applying repetitively the same operations on large arrays, a type of task likely to receive a substantial gain in speed when parallelized. Traditionally, parallelism has been achieved by increasing the number of CPUs in a computer, or the number of computing cores in each CPU.\\ Graphical Processing Units (GPU) may be viewed as a specialized type of processor with thousands of computing cores. Thanks to a combination of hardware and software developments, their use has expanded from the domain of computer graphics into that of general purpose computing. Besides commercial softwares written to take advantage of the computing power of GPUs, several tools exist to develop computer programs that offload parts of a computation to one or more GPUs.\\ Among these programming frameworks, CUDA (Compute Unified Device Architecture) is one of the most widely employed. CUDA is an extension of the C language, but can be used also in C, FORTRAN, Python and many other programming languages. The goal of this course is to expose the concepts of CUDA programming from the ground up, in order to write simple programs that use GPUs to accelerate critical sections of code.\\ Architecture of a GPU (computing units, memory, interplay with the host computer). What type of tasks are GPUs good for? Memory operations (allocation, data transfer to and from a GPU). GPU shared memory. CUDA kernels. Streams, synchronization between dependent tasks. Reduction operations on a GPU. Available libraries: cuFFT, cuBLAS, cuSPARSE, cuRAND.

Monday 30 September 2024, 14:15 at IPHT, Salle Claude Itzykson, Bât. 774 COURS (Cours) physics

François Gélis ( Le site de l'IPhT ) Introduction to CUDA programming for physicists

Abstract: Abstract:\newline Many numerical computations in physics require applying repetitively the same operations on large arrays, a type of task likely to receive a substantial gain in speed when parallelized. Traditionally, parallelism has been achieved by increasing the number of CPUs in a computer, or the number of computing cores in each CPU.\\ Graphical Processing Units (GPU) may be viewed as a specialized type of processor with thousands of computing cores. Thanks to a combination of hardware and software developments, their use has expanded from the domain of computer graphics into that of general purpose computing. Besides commercial softwares written to take advantage of the computing power of GPUs, several tools exist to develop computer programs that offload parts of a computation to one or more GPUs.\\ Among these programming frameworks, CUDA (Compute Unified Device Architecture) is one of the most widely employed. CUDA is an extension of the C language, but can be used also in C, FORTRAN, Python and many other programming languages. The goal of this course is to expose the concepts of CUDA programming from the ground up, in order to write simple programs that use GPUs to accelerate critical sections of code.\\ Architecture of a GPU (computing units, memory, interplay with the host computer). What type of tasks are GPUs good for? Memory operations (allocation, data transfer to and from a GPU). GPU shared memory. CUDA kernels. Streams, synchronization between dependent tasks. Reduction operations on a GPU. Available libraries: cuFFT, cuBLAS, cuSPARSE, cuRAND.

Monday 7 October 2024, 14:15 at IPHT, Salle Claude Itzykson, Bât. 774 COURS (Cours) physics

François Gélis ( Le site de l'IPhT ) Introduction to CUDA programming for physicists

Abstract: Abstract:\newline Many numerical computations in physics require applying repetitively the same operations on large arrays, a type of task likely to receive a substantial gain in speed when parallelized. Traditionally, parallelism has been achieved by increasing the number of CPUs in a computer, or the number of computing cores in each CPU.\\ Graphical Processing Units (GPU) may be viewed as a specialized type of processor with thousands of computing cores. Thanks to a combination of hardware and software developments, their use has expanded from the domain of computer graphics into that of general purpose computing. Besides commercial softwares written to take advantage of the computing power of GPUs, several tools exist to develop computer programs that offload parts of a computation to one or more GPUs.\\ Among these programming frameworks, CUDA (Compute Unified Device Architecture) is one of the most widely employed. CUDA is an extension of the C language, but can be used also in C, FORTRAN, Python and many other programming languages. The goal of this course is to expose the concepts of CUDA programming from the ground up, in order to write simple programs that use GPUs to accelerate critical sections of code.\\ Architecture of a GPU (computing units, memory, interplay with the host computer). What type of tasks are GPUs good for? Memory operations (allocation, data transfer to and from a GPU). GPU shared memory. CUDA kernels. Streams, synchronization between dependent tasks. Reduction operations on a GPU. Available libraries: cuFFT, cuBLAS, cuSPARSE, cuRAND.

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Monday 16 September 2024, 14:15 at IPHT, Salle Claude Itzykson, Bât. 774	COURS (Cours)	physics
François Gélis ( Le site de l'IPhT )	Introduction to CUDA programming for physicists
Abstract:	Abstract:\newline Many numerical computations in physics require applying repetitively the same operations on large arrays, a type of task likely to receive a substantial gain in speed when parallelized. Traditionally, parallelism has been achieved by increasing the number of CPUs in a computer, or the number of computing cores in each CPU.\\ Graphical Processing Units (GPU) may be viewed as a specialized type of processor with thousands of computing cores. Thanks to a combination of hardware and software developments, their use has expanded from the domain of computer graphics into that of general purpose computing. Besides commercial softwares written to take advantage of the computing power of GPUs, several tools exist to develop computer programs that offload parts of a computation to one or more GPUs.\\ Among these programming frameworks, CUDA (Compute Unified Device Architecture) is one of the most widely employed. CUDA is an extension of the C language, but can be used also in C, FORTRAN, Python and many other programming languages. The goal of this course is to expose the concepts of CUDA programming from the ground up, in order to write simple programs that use GPUs to accelerate critical sections of code.\\ Architecture of a GPU (computing units, memory, interplay with the host computer). What type of tasks are GPUs good for? Memory operations (allocation, data transfer to and from a GPU). GPU shared memory. CUDA kernels. Streams, synchronization between dependent tasks. Reduction operations on a GPU. Available libraries: cuFFT, cuBLAS, cuSPARSE, cuRAND.

Thursday 26 September 2024, 17:00 at UFR-PHYS-SU, Amphi 25 Campus Pierre-et-Marie-Curie, Jussieu	CPMC (Colloquium Pierre et Marie Curie)	astro-ph\|cond-mat\|gr-qc\|hep-ex\|hep-lat\|hep-ph\|hep-th\|physics\|quant-ph
Gérard Liger-Belair ( Université de Reims Champagne-Ardennes )	L'odyssée d'une bulle de champagne
Abstract:	TBA

Thursday 28 November 2024, 17:00 at UFR-PHYS-SU, Amphi 25 Campus Pierre-et-Marie-Curie, Jussieu	CPMC (Colloquium Pierre et Marie Curie)	astro-ph\|cond-mat\|gr-qc\|hep-ex\|hep-lat\|hep-ph\|hep-th\|physics\|quant-ph
Alberto Loarte ( Head of ITER Science Division )	ITER: objectives, status and physics of nuclear fusion plasmas
Abstract:	TBA

Thursday 30 January 2025, 17:00 at UFR-PHYS-SU, Amphi 25 Campus Pierre-et-Marie-Curie, Jussieu	CPMC (Colloquium Pierre et Marie Curie)	astro-ph\|cond-mat\|gr-qc\|hep-ex\|hep-lat\|hep-ph\|hep-th\|physics\|quant-ph
Bérengère Dubrulle ( SPEC Saclay, Académie des Sciences )	Turbulence et climat
Abstract:	TBA

Thursday 27 March 2025, 17:00 at UFR-PHYS-SU, Amphi 25 Campus Pierre-et-Marie-Curie, Jussieu	CPMC (Colloquium Pierre et Marie Curie)	astro-ph\|cond-mat\|gr-qc\|hep-ex\|hep-lat\|hep-ph\|hep-th\|physics\|quant-ph
Lydéric Bocquet ( LPENS, Académie des Sciences )	La mécanique moléculaire des fluides
Abstract:	TBA

Thursday 22 May 2025, 17:00 at UFR-PHYS-SU, Amphi 25 Campus Pierre-et-Marie-Curie, Jussieu	CPMC (Colloquium Pierre et Marie Curie)	astro-ph\|cond-mat\|gr-qc\|hep-ex\|hep-lat\|hep-ph\|hep-th\|physics\|quant-ph
Didier Queloz ( Cambridge U & ETH Zurich )	The exoplanet revolution
Abstract:	TBA