High Performance Scientific Computing

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HIGH PERFORMANCE SCIENTIFIC 3730209
FREE PDF 3730209
Code: 3730209 Credits: 3 Semester 3 CE

High Performance Scientific Computing

Get the full GTU study material, English & Gujarati textbook PDFs, syllabus curriculum, and solved previous year question papers.

High Performance Scientific Computing Syllabus — Unit-wise Content

The official GTU curriculum for High Performance Scientific Computing (subject code 3730209) as prescribed for CE · 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

3 lecture hours

  • Single Processor Computing
  • Parallel Computing
  • Parallel System Organization

Unit 2 Numerical Linear Algebra, High Performance Linear Algebra, Numerical

5 lecture hours

  • Treatment of Differential Equations

Unit 3 Applications

7 lecture hours

  • Molecular Dynamics
  • Graph Analytics
  • N-body Problems
  • Monte Carlo Methods
  • Computation Biology

Unit 4 Interactive Python using IPython, and the IPython Notebook, Python scripting

10 lecture hours

  • 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,

10 lecture hours

  • 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

13 lecture hours

  • 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

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