• Lectures: M W 9:30-10:45 LSB 1001
  Discussion Section: Thur 1:00-1:50 PHELP 2516, 4:00- 4:50 GIRV 1119 or 5:00- 5:50 PHELP 1425

• Instructor: Tao Yang (tyang at cs).
  Office Hours: Mon/Wed after class -11:45

• TAs:
  Shanxiu He (Office Hours: Fri 10:00 AM-12:00 PM, HFH 5106)
  Gyuwan Kim (Office Hours: Tue/Thu 3:00-4:00 PM, HFH 5106)
  Guanyu Yao (Office Hours: Mon 3:00-4:00 PM, Wed 3:00-4:00 PM, Henly Hall 2118)
  

• Parallel architecture, high performance computing, and performance evaluation
• Patterns of parallelism and program partitioning/mapping
• Distributed memory programming with message-passing interface (MPI)
• Shared memory programming with OpenMP and Pthreads
• Program parallelization of core algorithms in machine learning and scientific computing (matrix multiplication, equation solving, and model training)
• CUDA parallel programming with GPUs
• SIMD and cache-aware programming
• MapReduce and Spark programming for data intensive cloud computing

Prerequsites :

• Linear algebara (Math 4A).
• Programming experience with C on Linux.
• Knowledge of data structure and algorithms (mainly arrays and lists)
• Concepts: Algorithm complexity and partial derivatives

• Class schedule

• Class Policy

• References:
  
  • "An Introduction to Parallel Programming" by Peter Pacheco, 2011. Used in the past for this course. Author's website.
  • Introduction to High Performance Scientific Computing by David L. Chopp. SIAM 2019 Author's website
  • Additional online references are in weekly class schedule