Academic Courses

Educating the next generation of researchers through a unique scientific computing curriculum

TACC inspires and educates the next generation of computational scientists and technologists and increases the public's understanding of the roles computing and science play in shaping our society. To educate the next generation of researchers and computational professionals, TACC developed a unique scientific computing curriculum for The University of Texas at Austin.

In partnership with TACC, UT Austin offers five undergraduate and graduate level courses in supercomputing through the Department of Statistics and Scientific Computation in the College of Natural Sciences. The curriculum allows students to earn a Certificate of Scientific Computation (undergraduate students), or a Portfolio in Scientific Computation (graduate students), which is equivalent to earning a minor in the area.

TACC researchers, who are experts in high-performance computing, teach the courses, and students have the opportunity to apply the theories learned in class on TACC's HPC systems.

Students across engineering, science, mathematics, computer science, and liberal arts are all welcome.

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.

    -- ISP Course Materials

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.

    -- STC Course Materials

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.

    -- PCSE Course Materials

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.