Introduction to High Performance Computing (Slides)
Parallel Program Design
(Slides)
Parallel Program Design II
(Slides)
Introduction to OpenMP
(Slides)Introduction to MPI (Message Passing Interface) (Slides)
OpenACC
(Slides)
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems
Author(s):Dipanjan Sarkar, Raghav Bali, Tushar Sharma
Practical Machine Learning with Python
Dipanjan Sarkar, Raghav Bali, Tushar Sharma
Building Machine Learning Systems with Python
Author(s): Willi Richert, Luis Pedro Coelho
Author(s):Dipanjan Sarkar, Raghav Bali, Tushar Sharma
Practical Machine Learning with Python
Dipanjan Sarkar, Raghav Bali, Tushar Sharma
Building Machine Learning Systems with Python
Author(s): Willi Richert, Luis Pedro Coelho
Dr. Tassadaq Hussain.
www.tassadaq.ucerd.com
He is a permanent faculty member at, Riphah International University.
He did his Ph.D. from Barcelona-tech Spain, in collaboration with Barcelona Supercomputing Center and Microsoft Research Center.
He is a member of HiPEAC: European Network on High Performance and Embedded Architecture and Compilation, Barcelona Supercomputing Center and Microsoft ResearchCentre Spain.
Until January 2018, he had more than 14 years of industrial experience including, Barcelona Supercomputing Centre Spain, Infineon technology France, Microsoft Research Cambridge, PLDA Italia, IBM Zurich Switzerland, and REPSOL Spain. He has published more than 50 international publications and filed 5 patents.
Tassadaq's main research lines are Machine Learning, Parallel Programming, Heterogeneous Multi-core Architectures, Single board Computers, Embedded Computer Vision, Runtime Resource Aware Architectures, Software Defined Radio and Supercomputing for Artificial Intelligence and Scientific Computing.
www.tassadaq.ucerd.com
www.tassadaq.ucerd.com
He is a permanent faculty member at, Riphah International University.
He did his Ph.D. from Barcelona-tech Spain, in collaboration with Barcelona Supercomputing Center and Microsoft Research Center.
He is a member of HiPEAC: European Network on High Performance and Embedded Architecture and Compilation, Barcelona Supercomputing Center and Microsoft ResearchCentre Spain.
Until January 2018, he had more than 14 years of industrial experience including, Barcelona Supercomputing Centre Spain, Infineon technology France, Microsoft Research Cambridge, PLDA Italia, IBM Zurich Switzerland, and REPSOL Spain. He has published more than 50 international publications and filed 5 patents.
Tassadaq's main research lines are Machine Learning, Parallel Programming, Heterogeneous Multi-core Architectures, Single board Computers, Embedded Computer Vision, Runtime Resource Aware Architectures, Software Defined Radio and Supercomputing for Artificial Intelligence and Scientific Computing.
www.tassadaq.ucerd.com
Introduction to Parallel Programming Models
Innovations in computer system architecture, like hyperthreading or multicore processors, make parallel computing resources to solve different type of scientific problems. However, the use of these discoveries requires parallel programming techniques. Many standard
software products are based on concepts of parallel programming and use the hardware devices of future multicore processors efficiently. Thus, the need for parallel programming will extend to all areas of scientific applications. The application area will be much larger than the area of scientific computing, which used to be the main area of parallel computing for many years. The development of the application area for parallel computing will lead to a tremendous demand for software developers with parallel programming abilities. Some chip manufacturers already need to add parallel programming as a standard course in computer science curricula.
The course "Parallel Programming Models" discusses the computer architecture and state of the art programming approaches.
software products are based on concepts of parallel programming and use the hardware devices of future multicore processors efficiently. Thus, the need for parallel programming will extend to all areas of scientific applications. The application area will be much larger than the area of scientific computing, which used to be the main area of parallel computing for many years. The development of the application area for parallel computing will lead to a tremendous demand for software developers with parallel programming abilities. Some chip manufacturers already need to add parallel programming as a standard course in computer science curricula.
The course "Parallel Programming Models" discusses the computer architecture and state of the art programming approaches.
UCERD Rawalpindi
Supercomputing Center
UCERD Murree
UCERD Gathering Intellectuals Fostering Innovations
Unal Center of Educaiton Research & Development
Unal Center of Educaiton Research & Development
