Support for Classes
ACER frequently collaborates with various academic departments to provide Computing Resources and Services and evolve current curricula and introduce innovative HPC and Big Data technologies to students at early stages of their education. For HPC, ACER group has taught over six classes in the last two years where they provided hands-on training to over 125 doctoral, graduate and undergraduate students in a classroom environment. Please review our instructional use policy before requesting classroom accounts.
Some of the courses ACER has collaborated in the past include:
CS/ECE 566 - Parallel Processing Heading link
CS/ECE 566 — Parallel Processing:
Parallel processing from the computer science perspective. Includes Architecture (bus based, lockstep, SIMD), Programming Languages (Functional, traditional and extensions), compilers, interconnection networks, and algorithms. The course offers students an opportunity to develop expertise in parallel algorithms and program development while learning to utilize the compute resources on Extreme and SABER.
Instructors: Profs. Ajay Kshemkalyani and Shantanu Dutt | 2014 — 2019
PHYS 491 – Computational Methods in Biophysics Heading link
PHYS 491 — Computational Methods in Biophysics:
This course provides an introduction to computational biophysics with a focus on molecular dynamics simulations. Students will learn basic techniques of bio molecular simulations and its application on high-performance computing clusters. Lectures will be combined with practical exercises that applies computational methods and analysis to small projects. Registered students will have access to the campus high-performance computing cluster, Extreme, to run their simulations and analyze their data.
Instructor: Prof. Fatemah Khalili | Spring 2014, 2015
MCS 572 - Introduction to Supercomputing Heading link
MCS 572 — Introduction to Supercomputing:
Fundamentals of parallel and cluster computing pertinent to scientific computing, key issues of decomposing computations to exploit pipeline floating point units and code restructuring to minimize data traffic between processor and memory systems, parallel computing hardware options: experience using MPI, OpenMP and GPU.
Introduction–Parallel performance –Computer architecture–Dependences–Linear systems– Parallel languages–Collective operations–Current Programming standards–Advanced MPI–OpenMP (Multi Processing)–Machine and deep learning–Guest lectures and presentations
Instructor: Prof. Gerard Awanou | Spring 2019