Languages and Abstractions for High-Performance Scientific Computing (CS 598APK) Fall 2018

What	Where
Time/place	Wed/Fri 2:00pm-3:15pm 1109 Siebel / Catalog
Class URL	https://bit.ly/hpcabstr-f18
Class recordings	Echo 360
Piazza	Discuss »
Calendar	View »

Assignments

Current

• Project Submssion:

  • Poster Session: In class on Wednesday Dec 12 - CS Poster Printing Requests
  • Project Report and Materials Submission Deadline: Tuesday Dec 18 11pm
  • Poster Template (optional)

• Presentation Response Dec 7 (Due: Fri Dec 14)

• Presentation Response Dec 5 (Due: Wed Dec 12)
• Presentation Response Nov 30 (Due: Fri Dec 7)
• Paper Presentation: Slides Submission

Past

• Presentation Response Nov 28 (Due: Wed Dec 5)
• Presentation Response Nov 16 (Due: Fri Nov 30)
• HW4 (Due: Wed Nov 28)
• Presentation Response Nov 9 (Due: Fri Nov 16)
• Presentation Response Nov 2 (Due: Fri Nov 9)
• Consent for HW3 code release (Due: Mon Nov 5)
• Presentation Response Oct 26 (Due: Fri Nov 2)
• Presentation Response Oct 19 (Due: Fri Oct 26)

• HW3 (Due: Wed Oct 31)

  • Submissions: GPU Convolution

• Project Proposal (First draft due: Wed Oct 10)

• Presentation Response Oct 12 (Due: Fri Oct 19)

• HW2 (Due: Fri Sep 28) (Consent for code release)

  • Submissions: Pebbling
  • Submissions: SIMD Transpose

• Presentation Response Oct 5 (Due: Fri Oct 12)

• HW1 (Due: Fri Sep 14)
• Presentation Response Sep 21 (Due: Fri Sep 28)
• SSH Public Key Submssion (Due: Fri Sep 7)
• Talk topic selection (Due Fri Sep 7)

Paper Presentations

Paper presentations: Times/Topics/Slides (Times spreadsheet for posterity)

Why You Should Take this Class

Software for large-scale problems is stretched between three key requirements: high-performacne, typically parallel implementation, asymptotically optimal algorithms, and often highly technical application domains. This tension contributes considerably to making HPC software difficult to write and hard to maintain. If you are faced with this problem, this class can help you find and develop possible solution approaches.

Abstractions, tools, and languages can help restore separation of concerns and ease creation and maintenance of such software. Proven approaches to this problem include domain-specific mini-languages (`DSLs'), code generation, as well as 'active' libraries.

This class begins with a quick, but thorough examination of the problem setting: What machines are we realistically confronted with now and in the foreseeable future? What are determinants of performance? How can we measure and understand performance?

From the hardware level, we will then move towards a view of abstractions: Concepts and simplifications of complex hardware realities that are sufficiently thin to allow user/developer to reason about expected performance while achieving a substantial simplification of the programming task.

We will discuss, design, and evaluate a number of such systems, with the goal of putting you in a position to

• know the available landscape of tools
• make informed choices among them for a computational task at hand
• design your own abstractions and place them in context with respect to the state of the art.

As we progress, we will examine a number of increasingly high-level program representations, ranging from instruction sets to compiler IRs, through CUDA/OpenCL/'SIMT' models, to polyhedral and other higher-level representations. Along the way, you will design toy program representations and transformations for limited-scale tasks and examine your achievable and practically achieved performance.

We will pay careful attention to semantics and correctness in the context of program representation and ultimate code generation, but we will prefer the definition of specialized or simplified semantics over extensive compiler analyses that might help prove the validity of transformations.

To complement the many excellent distributed-memory offerings in the department (e.g. CS 484, CS 554), this class focuses more on 'on-node'/shared-memory performance.

Prerequisites / What You Should Already Know

• C
• Python
• Familiarity with parallel programming: You should have measured and wondered about the performance of a code you wrote
• Having taken a class on compiler construction (such as CS 426) will be helpful, but is not required.

While this class is being offered in a CS department, it is deliberately open to graduate students in the engineering disciplines who do extensive computational work and face this range of problems every day.

Suggested Papers for Student Presentations

See this list for an idea on the focus of this class. These papers can also serve as the basis for mid-semester paper presentations.

Instructor

Course Outline

I will insert links to class material, books, and papers into this tree as time goes on.

Note: the section headings in this tree are clickable to reveal more detail.

Computing

If you have submitted your SSH key, an account has been created you on a family of machines managed by members of the scientific computing area. See the linked page for more information on access and usage.

Virtual Machine Image

While you are free to install Python and Numpy on your own computer to do homework, the only supported way to do so is using the supplied virtual machine image.

Download Virtual Machine »

Grading Policies

View grading policies »