CS639 Undergraduate Elective Topics in Computing:
Parallel and Throughput-Optimized Programming

Spring Semester 2020


Course outline

Modern computing platforms offer dramatically increased computational capabilities compared to typical systems in relatively recent generations. Today, a consumer-grade gaming desktop can rival a typical medium-scale cluster from the early 2010’s, while a well-equipped single-chassis server may pack computational power commensurate with supercomputers that would appear in the TOP500 list about 8 years ago. This dramatic increase in computational density, however, comes with significant new challenges for the platform programmer that seeks to extract optimal performance: algorithms that exploit the full potential of modern computers need to be properly designed as to be in sync with parallel programming paradigms, and be more aware than ever of the idiosyncrasies of the underlying computing architecture.

This (new and experimental) course aspires to discuss challenges as well as best practices for the design of high-performance codes, with a depth and scope tailored to be accessible to undergraduates with modest programming experience. Rather than regarding the algorithm being used as an immutable specification (and focusing on the APIs that can help engineer a parallel implementation), we shall test and often alter then algorithmic approach itself in order to create better conditions for a high-efficiency parallel implementation. At the same time, we will attempt to deepen our awareness of the intricate architectural traits of the computing platform to better understand obstacles against and opportunities for optimal efficiency.

In the first offering of this Topics course in Spring 2020, the scope will be consciously kept narrow as to allow for adequate depth and analysis of the topics covered. Specifically, we will emphasize shared-memory, single-chassis multiprocessor systems as our primary target platform (with GPUs garnering some, but limited coverage), and forego highly heterogeneous platforms or distributed systems such as network-connected multi-node clusters. Our application focus and case studies will also be drawn primarily from numerical algorithms, scientific computing and computational engineering (with higher-level applications such as image processing, computational physics or machine learning naturally emerging from those). We will not explicitly emphasize combinatorial workloads (e.g. search and hashing), algorithms that rely heavily on unstructured, random data access, or applications that do not have high-throughput processing as a central design objective.

Programming paradigms, design practices, and platform considerations to be discussed in class may include:

Topics from which case studies and sample workloads will be drawn include the following facets of scientific computing:


General information

Lecture meeting time : Tue/Thu 2:30pm - 3:45pm
Lecture location : Grainger Hall 2080

Instructor : Eftychios Sifakis
Office : Computer Sciences building, Room 6387
Email : sifakis <at> cs <dot> wisc <dot> edu

Prerequisites : Working knowledge of the C programming language is presumed, as well as familiarity with principles of machine organization. CS354 or equivalent is strongly recommended (can be waived with instructor consent). Familiarity with basic linear algebra is desirable, but no formal prerequisite is enforced.


Schedule of lectures

DATE Lecture Information Assignments & Reading Materials

Tuesday, January 21st

Introduction to CS639
Discussion of course structure and logistics

Lecture Notes [PDF]

Thursday, January 23rd

Discussion of different types of Concurrency
Introduction to multithreading and OpenMP

Review Ruud van der Pas' OpenMP slides [PDF]

Tuesday, January 28th

Introduction to Stencil operations on Grids. Implementation and evaluation of variants of a Laplacian kernel.

Lecture Notes [PDF]

Thursday, January 30th

Stencil operations on Grids (continued discussion of Laplacian kernel). Introduction to Vectorization and SIMD processing.

Lecture Notes [PDF]

Tuesday, February 4th

Introduction to Vectorization and SIMD processing (cont'd). Introduction to Sparse Linear Solvers (using Stencils)

Lecture Notes [PDF]

Thursday, February 6th

Matrix-Free Sparse Solvers (Laplace equation Part #1)

Lecture Notes [PDF]

Tuesday, February 11th

Code Review : A Matrix-Free solver for the 3D Poisson Equation (Part I) (Factorization of code into kernels, reductions, and parallelization considerations)

Continuation of notes from Feb 4th. Review code in our repository, at subdirectory LaplaceSolver_0_1

Thursday, February 13th

Code Review : A Matrix-Free solver for the 3D Poisson Equation (Part II) (Kernel aggregation, aggregate timing). Introduction to Sparse Matrix Formats

Continuation of notes from Feb 4th.
Review code in our repository, at subdirectory LaplaceSolver_0_2 and LaplaceSolver_0_3. Start reading Edmond Chow's notes on Sparse Matrix Formats [PDF]