Cray Inc., a subsidiary of Hewlett Packard Enterprise, is an American supercomputer manufacturer headquartered in Seattle, Washington. It also manufactures systems for data storage and analytics. Several Cray supercomputer systems are listed in the TOP500, which ranks the most powerful supercomputers in the world.
Message Passing Interface (MPI) is a standardized and portable message-passing standard designed to function on parallel computing architectures. The MPI standard defines the syntax and semantics of library routines that are useful to a wide range of users writing portable message-passing programs in C, C++, and Fortran. There are several open-source MPI implementations, which fostered the development of a parallel software industry, and encouraged development of portable and scalable large-scale parallel applications.
Flynn's taxonomy is a classification of computer architectures, proposed by Michael J. Flynn in 1966 and extended in 1972. The classification system has stuck, and it has been used as a tool in the design of modern processors and their functionalities. Since the rise of multiprocessing central processing units (CPUs), a multiprogramming context has evolved as an extension of the classification system. Vector processing, covered by Duncan's taxonomy, is missing from Flynn's work because the Cray-1 was released in 1977: Flynn's second paper was published in 1972.
OpenMP is an application programming interface (API) that supports multi-platform shared-memory multiprocessing programming in C, C++, and Fortran, on many platforms, instruction-set architectures and operating systems, including Solaris, AIX, FreeBSD, HP-UX, Linux, macOS, and Windows. It consists of a set of compiler directives, library routines, and environment variables that influence run-time behavior.
Fortress is a discontinued experimental programming language for high-performance computing, created by Sun Microsystems with funding from DARPA's High Productivity Computing Systems project. One of the language designers was Guy L. Steele Jr., whose previous work includes Scheme, Common Lisp, and Java.
In computing, a parallel programming model is an abstraction of parallel computer architecture, with which it is convenient to express algorithms and their composition in programs. The value of a programming model can be judged on its generality: how well a range of different problems can be expressed for a variety of different architectures, and its performance: how efficiently the compiled programs can execute. The implementation of a parallel programming model can take the form of a library invoked from a sequential language, as an extension to an existing language, or as an entirely new language.
Automatic parallelization, also auto parallelization, or autoparallelization refers to converting sequential code into multi-threaded and/or vectorized code in order to use multiple processors simultaneously in a shared-memory multiprocessor (SMP) machine. Fully automatic parallelization of sequential programs is a challenge because it requires complex program analysis and the best approach may depend upon parameter values that are not known at compilation time.
VTune Profiler is a performance analysis tool for x86-based machines running Linux or Microsoft Windows operating systems. Many features work on both Intel and AMD hardware, but the advanced hardware-based sampling features require an Intel-manufactured CPU.
Compute Unified Device Architecture (CUDA) is a parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for accelerated general-purpose processing, an approach called general-purpose computing on GPUs (GPGPU). CUDA API and its runtime: The CUDA API is an extension of the C programming language that adds the ability to specify thread-level parallelism in C and also to specify GPU device specific operations (like moving data between the CPU and the GPU). CUDA is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements for the execution of compute kernels. In addition to drivers and runtime kernels, the CUDA platform includes compilers, libraries and developer tools to help programmers accelerate their applications.
In computer science, partitioned global address space (PGAS) is a parallel programming model paradigm. PGAS is typified by communication operations involving a global memory address space abstraction that is logically partitioned, where a portion is local to each process, thread, or processing element. The novelty of PGAS is that the portions of the shared memory space may have an affinity for a particular process, thereby exploiting locality of reference in order to improve performance. A PGAS memory model is featured in various parallel programming languages and libraries, including: Coarray Fortran, Unified Parallel C, Split-C, Fortress, Chapel, X10, UPC++, Coarray C++, Global Arrays, DASH and SHMEM. The PGAS paradigm is now an integrated part of the Fortran language, as of Fortran 2008 which standardized coarrays.
Data parallelism is parallelization across multiple processors in parallel computing environments. It focuses on distributing the data across different nodes, which operate on the data in parallel. It can be applied on regular data structures like arrays and matrices by working on each element in parallel. It contrasts to task parallelism as another form of parallelism.
PGI was a company that produced a set of commercially available Fortran, C and C++ compilers for high-performance computing systems. On July 29, 2013, Nvidia acquired The Portland Group, Inc. As of August 5, 2020, the "PGI Compilers and Tools" technology is a part of the Nvidia HPC SDK product available as a free download from Nvidia.
OpenCL is a framework for writing programs that execute across heterogeneous platforms consisting of central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs) and other processors or hardware accelerators. OpenCL specifies programming languages for programming these devices and application programming interfaces (APIs) to control the platform and execute programs on the compute devices. OpenCL provides a standard interface for parallel computing using task- and data-based parallelism.
OpenACC is a programming standard for parallel computing developed by Cray, CAPS, Nvidia and PGI. The standard is designed to simplify parallel programming of heterogeneous CPU/GPU systems.
C++ Accelerated Massive Parallelism is a native programming model that contains elements that span the C++ programming language and its runtime library. It provides an easy way to write programs that compile and execute on data-parallel hardware, such as graphics cards (GPUs).
Intel Advisor is a design assistance and analysis tool for SIMD vectorization, threading, memory use, and GPU offload optimization. The tool supports C, C++, Data Parallel C++ (DPC++), Fortran and Python languages. It is available on Windows and Linux operating systems in form of Standalone GUI tool, Microsoft Visual Studio plug-in or command line interface. It supports OpenMP. Intel Advisor user interface is also available on macOS.
ROCm is an Advanced Micro Devices (AMD) software stack for graphics processing unit (GPU) programming. ROCm spans several domains: general-purpose computing on graphics processing units (GPGPU), high performance computing (HPC), heterogeneous computing. It offers several programming models: HIP, OpenMP/Message Passing Interface (MPI), and OpenCL.
Performance portability refers to the ability of computer programs and applications to operate effectively across different platforms. Developers of performance portable applications seek to support multiple platforms without impeding performance, and ideally while minimizing platform-specific code.
The Center for Supercomputing Research and Development (CSRD) at the University of Illinois (UIUC) was a research center funded from 1984 to 1993. It built the shared memory Cedar computer system, which included four hardware multiprocessor clusters, as well as parallel system and applications software. It was distinguished from the four earlier UIUC Illiac systems by starting with commercial shared memory subsystems that were based on an earlier paper published by the CSRD founders. Thus CSRD was able to avoid many of the hardware design issues that slowed the Illiac series work. Over its 9 years of major funding, plus follow-on work by many of its participants, CSRD pioneered many of the shared memory architectural and software technologies upon which all 21st century computation is based.
