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Academic Courses

Scientific Computing Curriculum

To educate the next generation of researchers and computational professionals, TACC created a unique curriculum for The University of Texas at Austin which allows students to study supercomputing and earn a Certificate of Scientific Computation (undergraduate students), or a Portfolio in Scientific Computation (graduate students). TACC scientists teach five undergraduate and graduate level courses at UT Austin, in the Division of Statistics and Scientific Computation. Four of TACC's advanced computing courses are part of the requirements for the certification, which is the equivalent of a minor.

TACC researchers, who are experts in high-performance computing, teach the courses, and students have the opportunity to apply what they learn on TACC's HPC systems. Students across engineering, science, mathematics, computer science, and liberal arts are welcome.

Spring 2014 Courses

SSC 222/292 – Introduction to Scientific Programming
SSC 374C/394C – Parallel Computing for Science and Engineering

Fall 2013 Courses

SSC 322/392 (formerly 222/292) Intro to Scientific Programming
SSC 335/394 Scientific/Technical Computing
SSC 374E/394E Visualization & Data Analysis for Scientists & Engineers

Course Descriptions

SSC 322/392 (formerly 222/292) – Introduction to Scientific Programming

Introduction to programming using both the C and Fortran (95, 2003) languages, with applications to basic scientific problems. Covers common data types and structures, control structures, algorithms, performance measurement, and interoperability. Topics Covered: C and Fortran (95, 2003); Scientific problem applications; Common data types & structures; Control structures & algorithms; Performance measurement & interoperability

SSC 335/394 – Scientific/Technical Computing

Comprehensive introduction to computing techniques and methods applicable to many scientific disciplines and technical applications. Covers computer hardware and operating systems, systems software and tools, code development, numerical methods and math libraries, and basic visualization and data analysis tools. Three lecture hours a week for one semester.

SSC 374C/394C – Parallel Computing for Science and Engineering (PCSE)

Parallel computing principles, architectures, and technologies. Parallel application development, performance, and scalability. Prepares students to formulate and develop parallel algorithms to implement effective applications for parallel computing systems. Three lecture hours a week for one semester. Topics Covered: Principles, architectures & technologies; Parallel application development; Performance & scalability; Parallel algorithm formulation & development

SSC 374E/394E Visualization & Data Analysis for Scientists & Engineers

Scientific visualization principles, practices, and technologies, including remote and collaborative visualization. Introduces statistical analysis, data mining and feature detection. Prerequisite: Graduate standing, Mathematics 408D or 408M, Mathematics 340L, and prior programming experience using C or Fortran on Linux or Unix systems.

SSC 375/395 – High Performance Scientific Computing with GPUs

Comprehensive introduction to parallel programming of GPUs with CUDA in C and Fortran. Description of the function and parallelism of the architecture components of GPUs and introduction to CUDA as the programming paradigm. CUDA language elements are presented side-by-side with hardware components to guide towards an efficient use of both language and hardware. Three lecture hours a week for one semester.