Parallel Processing: Architecture Overview WW Grid Rajkumar Buyya Grid Computing and Distributed Systems (GRIDS) Lab. The University of Melbourne Melbourne, Australia www.gridbus.org

Serial Vs. Parallel COUNTER 2 COUNTER COUNTER 1 Q Please

Overview of the Talk Introduction Why Parallel Processing ? Parallel System H/W Architecture Parallel Operating Systems

Multi-Processor Computing System Computing Elements Applications Programming paradigms P . Microkernel Multi-Processor Computing System Threads Interface Operating System Hardware Process Processor Thread P

Two Eras of Computing Architectures System Software/Compiler Applications P.S.Es System Software Sequential Era Parallel Era 1940 50 60 70 80 90 2000 2030 Commercialization R & D Commodity

History of Parallel Processing The notion of parallel processing can be traced to a tablet dated around 100 BC. Tablet has 3 calculating positions capable of operating simultaneously. From this we can infer that: They were aimed at “speed” or “reliability”.

Motivating Factors Just as we learned to fly, not by constructing a machine that flaps its wings like birds, but by applying aerodynamics principles demonstrated by the nature... Similarly parallel processing has been modeled after those of biological species. Aggregated speed with which complex calculations carried out by (billions of) neurons demonstrate feasibility of PP. Individual neuron response speed is slow (ms) –

Why Parallel Processing? Computation requirements are ever increasing -- visualization, distributed databases, simulations, scientific prediction (earthquake), etc. Silicon based (sequential) architectures reaching physical limits in processing limits as they are constrained by: the speed of light, thermodynamics

Human Architecture! Growth Performance Vertical Horizontal Growth 5 10 15 20 25 30 35 40 45 . . . . Age

Computational Power Improvement Multiprocessor Uniprocessor C.P.I 1 2 . . . . No. of Processors

Why Parallel Processing? Hardware improvements like pipelining, superscalar are not scaling well and require sophisticated compiler technology to exploit performance out of them. Techniques such as vector processing works well for certain kind of problems.

Why Parallel Processing? Significant development in networking technology is paving a way for network-based cost-effective parallel computing. The parallel processing technology is mature and is being exploited commercially.

Parallel Programs Consist of multiple active “processes” simultaneously solving a given problem. And the communication and synchronization between them (parallel processes) forms the core of parallel programming efforts.

Types of Parallel Systems Tightly Couple Systems: Shared Memory Parallel Smallest extension to existing systems Program conversion is incremental Distributed Memory Parallel Completely new systems Programs must be reconstructed Loosely Coupled Systems: Clusters Built using commodity systems Centralised management Grids Aggregation of distributed systems Decentralized management

Processing Elements Architecture

Processing Elements Flynn proposed a classification of computer systems based on a number of instruction and data streams that can be processed simultaneously. They are: SISD (Single Instruction and Single Data) Conventional computers SIMD (Single Instruction and Multiple Data) Data parallel, vector computing machines MISD (Multiple Instruction and Single Data) Systolic arrays MIMD (Multiple Instruction and Multiple Data) General purpose machine

SISD : A Conventional Computer Processor Data Input Data Output Instructions Speed is limited by the rate at which computer can transfer information internally. Ex: PCs, Workstations

The MISD Architecture Data Input Stream Output Processor A B C Instruction Stream A Stream B Instruction Stream C More of an intellectual exercise than a practical configuration. Few built, but commercially not available

SIMD Architecture Instruction Stream Processor A B C Data Input stream A stream B stream C Data Output Ci<= Ai * Bi Ex: CRAY machine vector processing, Thinking machine cm* Intel MMX (multimedia support)

MIMD Architecture Instruction Stream A Instruction Stream B Instruction Stream C Data Output stream A Data Input stream A Processor A Data Output stream B Data Input stream B Processor B Data Output stream C Processor C Data Input stream C Unlike SISD, MISD, MIMD computer works asynchronously. Shared memory (tightly coupled) MIMD Distributed memory (loosely coupled) MIMD

Shared Memory MIMD machine Processor A Processor B Processor C MEMORY BUS MEMORY BUS MEMORY BUS Global Memory System Comm: Source PE writes data to GM & destination retrieves it Easy to build, conventional OSes of SISD can be easily be ported Limitation : reliability & expandibility. A memory component or any processor failure affects the whole system. Increase of processors leads to memory contention. Ex. : Silicon graphics supercomputers....

Distributed Memory MIMD IPC channel IPC channel Processor A Processor B Processor C MEMORY BUS MEMORY BUS MEMORY BUS Memory System A System B System C Communication : IPC (Inter-Process Communication) via High Speed Network. Network can be configured to ... Tree, Mesh, Cube, etc. Unlike Shared MIMD easily/ readily expandable Highly reliable (any CPU failure does not affect the whole system)

Laws of caution..... Speed of computation is proportional to the square root of system cost. i.e. Speed = Cost Speedup by a parallel computer increases as the logarithm of the number of processors. Speedup = log2(no. of processors) C S S P log2P

Caution.... Very fast development in network computing and related area have blurred concept boundaries, causing lot of terminological confusion : concurrent computing, parallel computing, multiprocessing, supercomputing, massively parallel processing, cluster computing, distributed computing, Internet computing, grid computing, etc. At the user level, even well-defined distinctions such as shared memory and distributed memory are disappearing due to new advances in technology. Good tools for parallel program development and debugging are yet to emerge.

Caution.... There is no strict delimiters for contributors to the area of parallel processing: computer architecture, operating systems, high-level languages, algorithms, databases, computer networks, … All have a role to play.

Operating Systems for High Performance Computing

Types of Parallel Systems Shared Memory Parallel Smallest extension to existing systems Program conversion is incremental Distributed Memory Parallel Completely new systems Programs must be reconstructed Clusters Slow communication form of Distributed

Operating Systems for PP MPP systems having thousands of processors requires OS radically different from current ones. Every CPU needs OS : to manage its resources to hide its details Traditional systems are heavy, complex and not suitable for MPP

Operating System Models Frame work that unifies features, services and tasks performed Three approaches to building OS.... Monolithic OS Layered OS Microkernel based OS Client server OS Suitable for MPP systems Simplicity, flexibility and high performance are crucial for OS.

Monolithic Operating System Application Programs System Services Hardware User Mode Kernel Mode Better application Performance Difficult to extend Ex: MS-DOS

Memory & I/O Device Mgmt Layered OS Application Programs Application Programs User Mode Kernel Mode System Services Memory & I/O Device Mgmt Process Schedule Hardware Easier to enhance Each layer of code access lower level interface Low-application performance Ex : UNIX

Traditional OS OS User Mode Kernel Mode OS Designer Application Programs User Mode Kernel Mode OS Hardware OS Designer

New trend in OS design Servers Microkernel User Mode Kernel Mode Application Programs Application Programs User Mode Kernel Mode Microkernel Hardware

Microkernel/Client Server OS (for MPP Systems) Application Thread lib. File Server Network Server Display Server User Kernel Microkernel Send Reply Hardware Tiny OS kernel providing basic primitive (process, memory, IPC) Traditional services becomes subsystems Monolithic Application Perf. Competence OS = Microkernel + User Subsystems Ex: Mach, PARAS, Chorus, etc.

Few Popular Microkernel Systems MACH, CMU PARAS, C-DAC Chorus QNX, (Windows)