High Performance Scientific Computing
Get the full GTU study material, English & Gujarati textbook PDFs, syllabus curriculum, and solved previous year question papers.
English Book & Chapters
English Book Coming Soon
We are working hard to add this material. Stay tuned for updates!
Follow on Instagram for UpdatesGTU Previous Year Papers
Papers Coming Soon
We are working hard to add this material. Stay tuned for updates!
Follow on Instagram for UpdatesHigh Performance Scientific Computing Syllabus — Unit-wise Content
The official GTU curriculum for High Performance Scientific Computing (subject code 3730209) as prescribed for CEE · Semester 3 divides the course into 6 units, covering 22 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (3730209)Unit 1 Introduction
- Single Processor Computing
- Parallel Computing
- Parallel System Organization
Unit 2 Numerical Linear Algebra, High Performance Linear Algebra, Numerical
- Treatment of Differential Equations
Unit 3 Applications
- Molecular Dynamics
- Graph Analytics
- N-body Problems
- Monte Carlo Methods
- Computation Biology
Unit 4 Interactive Python using IPython, and the IPython Notebook, Python scripting
- and its uses in scientific computing
- Subtleties of computer arithmetic that can affect program correctness
Unit 5 Fortran 90, a compiled language that is widely used in scientific computing,
- Makefiles for building software and checking dependencies
- Analyse the cost of data communication
- main memory, and how this memory hierarchy affects code performance
Unit 6 OpenMP on top of Fortran for parallel programming of shared memory
- such as a multicore laptop.
- MPI on top of Fortran for distributed memory parallel programming
- such as on a cluster
- Parallel computing in IPython
- unit tests
- regression tests
- verification and validation of computer codes
- Graphics and visualization of computational results using Python
Study on Android App:
