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Overall Score : 90 / 100

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Course Description

This course will introduce you to the multiple forms of parallelism found in modern Intel architecture processors and teach you the programming frameworks for handling this parallelism in applications. You will get access to a cluster of modern manycore processors (Intel Xeon Phi architecture) for experiments with graded programming exercises.This course can apply to various HPC and datacenter workloads and framework including artificial intelligence (AI). You will learn how to handle data parallelism with vector instructions, task parallelism in shared memory with threads, parallelism in distributed memory with message passing, and memory architecture parallelism with optimized data containers. This knowledge will help you to accelerate computational applications by orders of magnitude, all the while keeping your code portable and future-proof.Prerequisite: programming in C/C++ or Fortran in the Linux environment and Linux shell proficiency (navigation, file copying, editing files in text-based editors, compilation).

Instructor Details

Andrey Vladimirov

Andrey Vladimirov, Ph. D., is Head of High-Performance Computing Research at Colfax International. His primary interest is the application of modern computing technologies to computationally demanding scientific problems. Before joining Colfax, A. Vladimirov was involved in computational astrophysics research at Stanford University, North Carolina State University, and the Ioffe Institute (Russia), where he studied cosmic rays, collisionless plasmas and the interstellar medium using computer simulations. He is the lead author of a book on parallel programming and optimization, a regular contributor to the online resource Colfax Research, an author of invited papers in industry-leading publications, and an author or co-author of over 10 peer-reviewed publications in the fields of theoretical astrophysics and scientific computing.



131 total reviews

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By Martin A on 31-Aug-18

Nice course, really enjoyed every challenge in the course. Well laid out goals for a Computer Science student.

By Mykola S on 7-Nov-18

, . OpenMP MPI. , .

By Adel F on 21-Feb-19

Perfect course

By Dinesh A G on 3-Sep-18

A very, very helpful course for learning parallel computing.

By Piotr A on 3-Sep-18

Very good course.

By Denis A Z Q on 22-Mar-18

very nice course, good lecturer! I really enjoyed it!

By Dario G on 28-Apr-18

Good introductory material on parallel computing and HPC with latest Intel hardware. Topics like OpenMP and MPI could perhaps have been covered in more details. Good practice assignments. Looking forward to the second part of this course, which promises to be a hoot.

By Apostolos N P on 4-Jun-18


By Jijo T on 14-May-18

Please bring advance courses by Intel also such as FPGA based courses.

By Alberto C B on 9-Mar-18

Very well explained, very simple, well structured and interesting examples. Although, more diversity of the examples would be interesting, for example, mathematical operations (as matrix-matrix multiplication), bioinformatics, financial, and so on. Also, would be great more exercises, labs and resources.

By Andronik on 17-Apr-19

I really learned a lot and enjoyed this course. I am much better versed at factorization, openMP and MPI as a result. I had experience with GPU programming but the methods here are vital for high end CPUs

By Prachi C on 11-Jun-19

Best course to understand the basics of parallel programming , this course covers the areas where parallelism can be performed and the hands on exercises hones your skills of what you have learnt. It is worth to pay for certification, because it gives you graded software tools to evaluate your performance on given tasks.