Introduction

Introduction

What class is this? CS 484 – Parallel Processing

Who am I? Quinn Snell 3366 TMCB [email protected] 422-5098

When are office hours? By appointment

Course and Text

Homepage (Everything is here)BYU Learning Suite

Text:Online tutorials & papers

Course Objectives

Students will understand and demonstrate the ability to develop Shared memory parallel programs (OpenMP & Pthreads), Distributed memory parallel programs (MPI), and Data parallel programs (CUDA)

Students will understand and be able to implement several basic parallel algorithms and load balancing techniques.

Lectures

Follow schedule on the homepage We will move quickly.

READ BEFORE CLASS!!!!!There will be in-class quizzes - no makeup

I will post lecture notes on the web.

What is expected of you? READ!!! Assignments

C/C++ on the Supercomputer & CS Open Labs All assignments include a report. The report IS

the assignment. The program is what you did to accomplish the assignment. - I will grade your writing.

Emailed to [email protected] Exams

In the testing center 8 1/2 x 11 sheet of notes allowed

What is expected of you?

Get an account on the university supercomputers Go to marylou.byu.edu Register Do it today!!!

Read through the batch jobs tutorial for marylou Schedule a simple hello world using a PBS script.

copy ~qos/hello/hello.c and ~qos/hello/hello.pbs edit hello.pbs mpicc hello.c qsub hello.pbs

Grading

Grade Distribution Quizzes 5 % Homework & labs 55 % Midterm 20 % Final 20 %

Grade Scale 94 % - Above A 90 % - 93.9 % A- 80 % - 89.9 % B-, B, B+ 65 % - 79.9 % C-, C, C+

I expect everyone to get a good grade!

Policies

Late Work I don’t like late work. However….. 10% off per school day – limited to 70% off

Programming Assignments To get better than a C-, you must complete ALL labs If you don’t complete all the labs, your grade ceiling is

a C-

Other Policies

Honor Code: I expect you to follow the honor code including the

dress and grooming standards. You can work together in groups on the homework

and laboratories from a conceptual perspective, but the answers that you give and the programs that you write should be your own, not copies of other students work.

Your reports should focus on your own ideas and things you have learned from your experimentation.

Other Policies

Cheating Cheating in any form will NOT be tolerated. This

includes copying any part of a homework assignment or programming lab.

Any assignment turned in that is not your work will be given a negative full value score. Any cases of multiple offense or cheating on a test will result in failure of the class.

Systems Abuse Policy Abuse in any form will result in immediate suspension

of your accounts(s).

Other Policies

Preventing Sexual Harassment Title IX of the Education Amendments of 1972 prohibits sex

discrimination against any participant in an educational program or activity receiving federal funds. Title IX covers discrimination in programs, admissions, activities, and student-to-student sexual harassment.

BYU's policy against sexual harassment extends not only to employees but to students as well.

If you encounter unlawful sexual harassment or gender based discrimination, please talk to your professor; contact the Equal Employment Office at 377-5895 or 367-5629 (24 hours); or contact the Honor Code Office at 378-2847.

Other Policies

Students With Disabilities Brigham Young University is committed to providing a

working and learning atmosphere that reasonably accommodates qualified persons with disabilites. If you have any disability, which may impair your ability to complete this course successfully, please contact the Services for Students with Disabilities Office (378-2767). Reasonable academic accommodations are reviewed for all students who have qualified documented disabilites.

Other Policies

Children in the Classroom The study of Computer Science requires a degree of

concentration and focus that is exceptional. Having small children in class is often a distraction that degrades the educational experience for the whole class. Please make other arrangements for child care rather than bringing children to class with you. If there are extenuating circumstances, please talk with your instructor in advance.

What will we do in this class? Focus on shared memory and message passing

programming PThreads OpenMP MPI Data parallel programming with CUDA

Write code for the supercomputers

Study parallel algorithms and parallelization

What is Parallelism? Multiple tasks working at the same time on

the same problem.

Parallel Computing

What is a parallel computer? A set of processors that are able to work

cooperatively to solve a computational problem Examples

Parallel supercomputers Clusters of workstations Symmetric multiprocessors Multiple core processors

Won't serial computers be fast enough? Moore's Law

Double in speed every 18 months

Predictions of need British government in 1940s predicted they would only

need about 2-3 computers Market for Cray was predicted to be about 10

Problem Doesn't take into account new applications.

Applications Drive Supercomputing Traditional

Weather simulation and prediction Climate modeling Chemical and physical computing

New apps. Collaborative environments DNA Virtual reality Parallel databases Games Photoshop

Application Needs

Graphics 109 volume elements 200 operations per element Real-time display

Weather & Climate 10 year simulation involves 1016 operations Accuracy can be improved by higher resolution grids

which involves more operations.

Cost-Performance Trend

1960s

1970s1980s

1990s

Cost

Performance

Serial computers

What does this suggest?

More performance is easy to a point. Significant performance increases of current

serial computers beyond the saturation point is extremely expensive.

Connecting large numbers of microprocessors into a parallel computer overcomes the saturation point. Cost stays low and performance increases.

Computer Design

Single processor performance has been increased lately by increasing the level of internal parallelism. Multiple functional units Pipelining

Higher performance gains by incorporating multiple "computers on a chip.”

Gigahertz race is over (Intel won) Multiple cores is where performance will come from

Computer Performance

1e2

1e7

1e12

Eniac

IBM 704

IBM 7090

CDC 7600

Cray 1Cray X-MP

Cray C90

IBM SP-2

1950 20001975

TFLOPS

Communication Performance

Early 1990s Ethernet 10 Mbps Mid 1990s FDDI 100 Mbps Mid 1990s ATM 100s Mbps Late 1990s Fast Ethernet 100 Mbps Late 1990s Gig Ethernet 100s Mps Gbps is now commonplace

Performance Summary

Applications are demanding more speed. Performance trends

Processors are increasing in speed. Communication performance is increasing.

Future Performance trends suggest a future where

parallelism pervades all computing. Concurrency is key to performance increases.

Parallel Processing Architectures Architectures

Single computer with lots of processors Multiple interconnected computers

Architecture governs programming Shared memory and locks Message passing

Shared Memory Computers

Uniform Memory Access All access the same

memory can lead to memory

bottleneck

Non-uniform Memory Access different speed access more scalable

Message Passing Computers

Distributed Shared Memory

Interconnection Network

Processors

Memory Modules

Message Passing Architectures

Requires some form of interconnection

The network is the bottleneckLatency and bandwidthDiameterBisection bandwidth


Line/Ring

Mesh/Torus


Tree/Fat Tree


Hypercube


Switched

Nice, but also has limitations

Parallel Programming Properties

Concurrency Performance should increase by employing

multiple processors. Scalability

Performance should continue to increase as we add more processors.

Locality of Reference Performance will be greater if we only access local

memory. Architecture affects each of the above!

Parallel Programming Models

Threads


Message Passing


Data Parallel

So What?

In this class you will learn to program using each of the parallel programming models.

We will talk about advantages/disadvantages of each model

We will also learn about common parallel algorithms and techniques

Introduction

Documents