1. Hank Childs, University of Oregon Jan. 30 th, 2014 CIS 610: Vector Models for Data-Parallel Computing

2. Class Organization Who will lecture next week? Alternate ideas for organizing lectures?

3. Material for Today’s Lecture A.K.A, “Data Parallel Bible” according to Chris Sewell Material & figures in this lecture almost uniformly come from this dissertation

4. History Lesson via Google Scholar

5. Machine Models Machine models you likely know about – Random access machine (RAM) model – Turing machine model This thesis defines a new model: parallel vector model – Defined in terms of machine architecture

6. Parallel Vector Models

7. Machine specifics V-RAM = S-RAM + vector memory & vector processor Each instruction of the vector processor operates on – Fixed number of vectors from the vector memory – Possibly scalars from scalar memory Example vector instruction: sum the elements of two vectors

8. Premise Claim: parallel vector models are the right abstraction to connect theory, languages, and architecture Specifically: – Can be mapped onto a broad variety of architectures – Can serve as algorithmic models to analyze complexity – Can serve as instruction sets for virtual machine for higher-level programming languages

10. Complexity Two main notions of complexity – Step complexity # of steps executed by a program – Element complexity Sum over all steps of the lengths of all vectors manipulated in each step Important: all data-parallel primitives considered have to finish in O(log N) time for N elements.

11. Complexity Example Step #1: – Sum two vectors of size 10 Step #2: – Square vector of length 100 What is Step Complexity? What is Element Complexity? What is Step Complexity? What is Element Complexity? 2 120 2 120

12. Some New Vector Instructions…

13. Scan Instruction

14. Segmented Instructions It is a little fuzzy to me how to do this in practice…

15. Quicksort

16. Quicksort pseudo-code OK, let’s try to do quicksort.

17. Rest of Book