Lecture 1 TTH 03:30AM-04:45PM Dr. Jianjun Hu CSCE569 Parallel Computing University of South Carolina Department of Computer Science and Engineering

CSCE569 Course Information Meet time: TTH 03:30AM-04:45PM Swearingen 2A21 4 Homework Use CSE turn-in system to submit your Homework ( Deadline policy 1 Midterm Exam (conceptual understanding) 1 Final Project (deliverable to your future employer!) Teamwork Implementation project/research project TA: No TA.

CSCE569 Course Information Textbook and references Parallel Programming: for Multicore and Cluster Systems By:Thomas Rauber (Author), Gudula Rünger (Author) Publisher: Springer; 1st Edition. edition (March 10, 2010) Good reference book: Parallel Programming in C with MPI and OpenMP by Michael J. QuinnMichael J. Quinn Most important information sources: Slides. Grading policy 4 homeworks, 1 midterm, 1 final project, in-class participation

About Your Instructor Dr. Jianjun Hu Office hours: TTH 2:30-3:20PM or Drop by any time Office Phone#: A66 SWNG Background: Mechanical Engineering/CAD Machine learning/Computational intelligence/Genetic Algorithms/Genetic Programming (PhD) Bioinformatics and Genomics (Postdoc) Multi-disciplinary just as parallel computing app.

Outline Motivation Modern scientific method Evolution of supercomputing Modern parallel computers Seeking concurrency Data clustering case study Programming parallel computers

Why You are Here? Solve BIG problems Use Supercomputers Write parallel programs

Why Faster Computers? Solve compute-intensive problems faster Make infeasible problems feasible Reduce design time Solve larger problems in same amount of time Improve answer’s precision Reduce design time Gain competitive advantage

Why Parallel Computing? The massively parallel architecture of GPUs, coming from its graphics heritage, is now delivering transformative results for scientists and researchers all over the world. For some of the world’s most challenging problems in medical research, drug discovery, weather modeling, and seismic exploration – computation is the ultimate tool. Without it, research would still be confined to trial and error-based physical experiments and observation.

What problems need Parallel Computing?

Parallel Computing in the Real-world Engineering Science Business Game Cloud-computing

What This course can do for You? Understanding of parallel computer architectures Developing parallel programs for both clusters and shared memory multi-core system MPI/OpenMP Know basics of CUDA programming Learn to do performance analysis of parallel programs

Definitions Parallel computing Using parallel computer to solve single problems faster Parallel computer Multiple-processor/core system supporting parallel programming Parallel programming Programming in a language that supports concurrency explicitly

Classical Science Nature Observation Theory Physical Experimentation

Modern Scientific Method Nature Observation Theory Physical Experimentation Numerical Simulation

Evolution of Supercomputing World War II Hand-computed artillery tables Need to speed computations ENIAC Cold War Nuclear weapon design Intelligence gathering Code-breaking

Supercomputer General-purpose computer Solves individual problems at high speeds, compared with contemporary systems Typically costs $10 million or more Traditionally found in government labs

Commercial Supercomputing Started in capital-intensive industries Petroleum exploration Automobile manufacturing Other companies followed suit Pharmaceutical design Consumer products

CPUs 1 Million Times Faster Faster clock speeds Greater system concurrency Multiple functional units Concurrent instruction execution Speculative instruction execution

Systems 1 Billion Times Faster Processors are 1 million times faster Combine thousands of processors Parallel computer Multiple processors Supports parallel programming Parallel computing = Using a parallel computer to execute a program faster

Beowulf Concept NASA (Sterling and Becker) Commodity processors Commodity interconnect Linux operating system Message Passing Interface (MPI) library High performance/$ for certain applications

Computing speed of supercomputers

Projected Computing speed of supercomputers

Top 10 Supercomputers GPU

What you can use Hardware Multicore chips (2011: mostly 2 cores and 4 cores, but doubling) (cores=processors) Servers (often 2 or 4 multicores sharing memory) Clusters (often several, to tens, and many more servers not sharing memory) Supercomputer at USC CEC

Supercomputers at USC CEC 76 Compute Nodes w/ dual 3.4 GHz 64 Nodes: Dual CPU

Supercomputers at USC CEC SGI Altix 4700 Shared-memory system Hardware 128 Itanium 1.6 GHz/ 8MB Cache 256 GB RAM 8TB storage NUMAlink Interconnect Fabric Software SUSE10 w/SGI PROPACK Intel C/C++ and Fortran Compilers VASP PBSPro scheduling software Message Passing Toolkit Intel Math Kernel Library GNU Scientific Library Boost library

Some historical machines

Earth Simulator was #1

Some interesting hardware Nvidia Cell Processor Sicortex – “Teraflops from Milliwatts”

GPU-based supercomputing+CUDA

Topic1: Hardware Architecture of parallel computing system

Topic2: Programming/Software Common parallel computing methods PBS- job scheduling system MPI: The Message Passing Interface Low level “lowest common denominator” language that the world has stuck with for nearly 20 years Can get performance, but can be a hindrance as well Pthread for multi-core shared memory parallel programming CUDA GPU programming MapReduce Google style high-performance computing

Why MPI? MPI = “Message Passing Interface” Standard specification for message-passing libraries Libraries available on virtually all parallel computers Free libraries also available for networks of workstations or commodity clusters

Why OpenMP? OpenMP an application programming interface (API) for shared-memory systems Supports higher performance parallel programming of symmetrical multiprocessors

Topic3: Performance Single processor speeds for now no longer growing. Moore’s law still allows for more real estate per core (transistors double/nearly every two years) People want performance but hard to get Slowdowns seen before speedups Flops (floating point ops / second) Gigaflops (10 9 ), Teraflops (10 12 ),Petaflops(10 15 )

Summary (1/2) High performance computing U.S. government Capital-intensive industries Many companies and research labs Parallel computers Commercial systems Commodity-based systems

Summary (2/2) Power of CPUs keeps growing exponentially Parallel programming environments changing very slowly Two standards have emerged MPI library, for processes that do not share memory OpenMP directives, for processes that do share memory

Places to Look Best current news: Huge Conference: Top500.org