Lecture

Parallel Computing: Principles and OpenMP

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In course

CS-307: Introduction to multiprocessor architecture

Multiprocessors are a core component in all types of computing infrastructure, from phones to datacenters. This course will build on the prerequisites of processor design and concurrency to introduce the essential technologies required to combine multiple ...

Description

This lecture introduces the principles of parallel computing, emphasizing the importance of parallelism in achieving higher performance due to the limitations of single-core processors. It covers the basics of OpenMP, a high-level API for shared-memory programming, and provides examples of parallelizing code using OpenMP directives. The lecture discusses the concepts of division of work, communication, synchronization, and execution models in parallel programming. It also explores the different communication models such as shared memory, message passing, and data parallelism. The presentation includes detailed explanations and examples of shared vs. private variables, synchronization techniques like critical sections and barriers, and the use of OpenMP for creating concurrent code from serial code.

Instructor

Babak Falsafi

Babak is a Professor in the School of Computer and Communication Sciences and the founding director of the EcoCloud, an industrial/academic consortium at EPFL investigating scalable data-centric technologies. He has made numerous contributions to computer system design and evaluation including a scalable multiprocessor architecture which was prototyped by Sun Microsystems (now Oracle), snoop filters and memory streaming technologies that are incorporated into IBM BlueGene/P and Q and ARM cores, and computer system performance evaluation methodologies that have been in use by AMD, HP and Google PerKit . He has shown that hardware memory consistency models are neither necessary (in the 90's) nor sufficient (a decade later) to achieve high performance in multiprocessor systems. These results eventually led to fence speculation in modern microprocessors. His latest work on workload-optimized server processors laid the foundation for the first generation of Cavium ARM server CPUs, ThunderX. He is a recipient of an NSF CAREER award, IBM Faculty Partnership Awards, and an Alfred P. Sloan Research Fellowship. He is a fellow of IEEE and ACM.

Official source

Ontological neighbourhood

Computer engineering

High-performance computing: Parallel computing

Related concepts (32)

Parallel computing

Parallel computing is a type of computation in which many calculations or processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has long been employed in high-performance computing, but has gained broader interest due to the physical constraints preventing frequency scaling.

Synchronization (computer science)

In computer science, synchronization refers to one of two distinct but related concepts: synchronization of processes, and synchronization of data. Process synchronization refers to the idea that multiple processes are to join up or handshake at a certain point, in order to reach an agreement or commit to a certain sequence of action. Data synchronization refers to the idea of keeping multiple copies of a dataset in coherence with one another, or to maintain data integrity.

Embarrassingly parallel

In parallel computing, an embarrassingly parallel workload or problem (also called embarrassingly parallelizable, perfectly parallel, delightfully parallel or pleasingly parallel) is one where little or no effort is needed to separate the problem into a number of parallel tasks. This is often the case where there is little or no dependency or need for communication between those parallel tasks, or for results between them. Thus, these are different from distributed computing problems that need communication between tasks, especially communication of intermediate results.

Massively parallel

Massively parallel is the term for using a large number of computer processors (or separate computers) to simultaneously perform a set of coordinated computations in parallel. GPUs are massively parallel architecture with tens of thousands of threads. One approach is grid computing, where the processing power of many computers in distributed, diverse administrative domains is opportunistically used whenever a computer is available. An example is BOINC, a volunteer-based, opportunistic grid system, whereby the grid provides power only on a best effort basis.

Multi-core processor

A multi-core processor is a microprocessor on a single integrated circuit with two or more separate processing units, called cores, each of which reads and executes program instructions. The instructions are ordinary CPU instructions (such as add, move data, and branch) but the single processor can run instructions on separate cores at the same time, increasing overall speed for programs that support multithreading or other parallel computing techniques.

Related lectures (63)

Parallel Programming I

Covers the basics of parallel programming, including concurrency, forms of parallelism, synchronization, and programming models like PThreads and OpenMP.

Synchronization

Covers the principles of synchronization in parallel computing, focusing on shared memory synchronization and different methods like locks and barriers.

Parallelism: Programming and Performance

Explores parallelism in programming, emphasizing trade-offs between programmability and performance, and introduces shared memory parallel programming using OpenMP.

Synchronization: Hardware Implementation

Explores hardware synchronization methods, including locks, barriers, and critical sections in parallel computing.

Principles of Parallel Computing: OpenMP