X10 (programming language)

Last updated
X10
Paradigm Object-oriented
Designed by Kemal Ebcioğlu, Vijay Saraswat, Saravanan Arumugam and Vivek Sarkar
Developer IBM
First appeared2004
Stable release
2.6.1 / July 30, 2017;15 months ago (2017-07-30)
Typing discipline Static, strong, safe, constrained
OS IBM AIX, Linux, Mac OS X, Windows
License Eclipse Public License 1.0
Filename extensions .x10
Website x10-lang.org
Influenced by
C++, Java

X10 is a programming language being developed by IBM at the Thomas J. Watson Research Center as part of the Productive, Easy-to-use, Reliable Computing System (PERCS) project funded by DARPA's High Productivity Computing Systems (HPCS) program. Its primary authors are Kemal Ebcioğlu, Vijay Saraswat, Saravanan Arumugam and Vivek Sarkar. [1]

Programming language language designed to communicate instructions to a machine

A programming language is a formal language, which comprises a set of instructions used to produce various kinds of output. Programming languages are used in computer programming to create programs that implement specific algorithms.

IBM American multinational technology and consulting corporation

International Business Machines Corporation (IBM) is an American multinational information technology company headquartered in Armonk, New York, with operations in over 170 countries. The company began in 1911, founded in Endicott, New York, as the Computing-Tabulating-Recording Company (CTR) and was renamed "International Business Machines" in 1924.

Thomas J. Watson Research Center research institute in Yorktown Heights, New York, USA

The Thomas J. Watson Research Center is the headquarters for IBM Research. The center comprises two sites, with its main laboratory in Yorktown Heights, New York, U.S., 38 miles (61 km) north of New York City and with offices in Cambridge, Massachusetts.

Contents

X10 is designed specifically for parallel computing using the partitioned global address space (PGAS) model. A computation is divided among a set of places, each of which holds some data and hosts one or more activities that operate on those data. It has a constrained type system for object-oriented programming, a form of dependent types. Other features include user-defined primitive struct types; globally distributed arrays, and structured and unstructured parallelism. [2]

Parallel computing programming paradigm in which many calculations or the execution of processes are carried out simultaneously

Parallel computing is a type of computation in which many calculations or the execution of 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 it's gaining broader interest due to the physical constraints preventing frequency scaling. As power consumption by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.

In computer science, a partitioned global address space (PGAS) is a parallel programming model. It assumes a global memory address space that is logically partitioned and a portion of it 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. The PGAS model is the basis of Unified Parallel C, Coarray Fortran, Split-C, Fortress, Chapel, X10, UPC++, Coarray C++, Global Arrays, DASH and SHMEM. In standard Fortran, this model is now an integrated part of the language. PGAS attempts to combine the advantages of a SPMD programming style for distributed memory systems with the data referencing semantics of shared memory systems. This is more realistic than the traditional shared memory approach of one flat address space, because hardware-specific data locality can be modeled in the partitioning of the address space.

X10 uses the concept of parent and child relationships for activities to prevent the lock stalemate that can occur when two or more processes wait for each other to finish before they can complete. An activity may spawn one or more child activities, which may themselves have children. Children cannot wait for a parent to finish, but a parent can wait for a child using the finish command. [3]

See also

Chapel (programming language) programming language

Chapel, the Cascade High Productivity Language, is a parallel programming language developed by Cray. It is being developed as part of the Cray Cascade project, a participant in DARPA's High Productivity Computing Systems (HPCS) program, which had the goal of increasing supercomputer productivity by the year 2010. It is being developed as an open source project, under version 2 of the Apache license.

Coarray Fortran (CAF), formerly known as F--, started as an extension of Fortran 95/2003 for parallel processing created by Robert Numrich and John Reid in the 1990s. The Fortran 2008 standard now includes coarrays, as decided at the May 2005 meeting of the ISO Fortran Committee; the syntax in the Fortran 2008 standard is slightly different from the original CAF proposal.

Concurrency (computer science)

In computer science, concurrency refers to the ability of different parts or units of a program, algorithm, or problem to be executed out-of-order or in partial order, without affecting the final outcome. This allows for parallel execution of the concurrent units, which can significantly improve overall speed of the execution in multi-processor and multi-core systems. In more technical terms, concurrency refers to the decomposability property of a program, algorithm, or problem into order-independent or partially-ordered components or units.

Related Research Articles

Earth Simulator highly parallel vector supercomputer system for running global climate models

The Earth Simulator (ES), developed by the Japanese government's initiative "Earth Simulator Project", was a highly parallel vector supercomputer system for running global climate models to evaluate the effects of global warming and problems in solid earth geophysics. The system was developed for Japan Aerospace Exploration Agency, Japan Atomic Energy Research Institute, and Japan Marine Science and Technology Center (JAMSTEC) in 1997. Construction started in October 1999, and the site officially opened on 11 March 2002. The project cost 60 billion yen.

OpenMP open standard for parallelizing

OpenMP is an application programming interface (API) that supports multi-platform shared memory multiprocessing programming in C, C++, and Fortran, on most platforms, instruction set architectures and operating systems, including Solaris, AIX, HP-UX, Linux, macOS, and Windows. It consists of a set of compiler directives, library routines, and environment variables that influence run-time behavior.

In computer science, type safety is the extent to which a programming language discourages or prevents type errors. A type error is erroneous or undesirable program behaviour caused by a discrepancy between differing data types for the program's constants, variables, and methods (functions), e.g., treating an integer (int) as a floating-point number (float). Type safety is sometimes alternatively considered to be a property of a computer program rather than the language in which that program is written; that is, some languages have type-safe facilities that can be circumvented by programmers who adopt practices that exhibit poor type safety. The formal type-theoretic definition of type safety is considerably stronger than what is understood by most programmers.

Cilk, Cilk++ and Cilk Plus are general-purpose programming languages designed for multithreaded parallel computing. They are based on the C and C++ programming languages, which they extend with constructs to express parallel loops and the fork–join idiom.

Unified Parallel C (UPC) is an extension of the C programming language designed for high-performance computing on large-scale parallel machines, including those with a common global address space and those with distributed memory. The programmer is presented with a single shared, partitioned address space, where variables may be directly read and written by any processor, but each variable is physically associated with a single processor. UPC uses a single program, multiple data (SPMD) model of computation in which the amount of parallelism is fixed at program startup time, typically with a single thread of execution per processor.

SIGPLAN is the Association for Computing Machinery's Special Interest Group on programming languages.

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, autoparallelization, or parallelization, the last one of which implies automation when used in context, refers to converting sequential code into multi-threaded or vectorized code in order to utilize multiple processors simultaneously in a shared-memory multiprocessor (SMP) machine. The goal of automatic parallelization is to relieve programmers from the hectic and error-prone manual parallelization process. Though the quality of automatic parallelization has improved in the past several decades, fully automatic parallelization of sequential programs by compilers remains a grand challenge due to its need for complex program analysis and the unknown factors during compilation.

Concurrent computing is a form of computing in which several computations are executed during overlapping time periods—concurrently—instead of sequentially. This is a property of a system—this may be an individual program, a computer, or a network—and there is a separate execution point or "thread of control" for each computation ("process"). A concurrent system is one where a computation can advance without waiting for all other computations to complete.

Data parallelism

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.

Task parallelism is a form of parallelization of computer code across multiple processors in parallel computing environments. Task parallelism focuses on distributing tasks—concurrently performed by processes or threads—across different processors. In contrast to data parallelism which involves running the same task on different components of data, task parallelism is distinguished by running many different tasks at the same time on the same data. A common type of task parallelism is pipelining which consists of moving a single set of data through a series of separate tasks where each task can execute independently of the others.

Explicit data graph execution, or EDGE, is a type of instruction set architecture (ISA) which intends to improve computing performance compared to common processors like the Intel x86 line. EDGE combines many individual instructions into a larger group known as a "hyperblock". Hyperblocks are designed to be able to easily run in parallel.

IPython

IPython is a command shell for interactive computing in multiple programming languages, originally developed for the Python programming language, that offers introspection, rich media, shell syntax, tab completion, and history. IPython provides the following features:

Parallel Extensions

Parallel Extensions was the development name for a managed concurrency library developed by a collaboration between Microsoft Research and the CLR team at Microsoft. The library was released in version 4.0 of the .NET Framework. It is composed of two parts: Parallel LINQ (PLINQ) and Task Parallel Library (TPL). It also consists of a set of coordination data structures (CDS) – sets of data structures used to synchronize and co-ordinate the execution of concurrent tasks.

In computing, algorithmic skeletons, or parallelism patterns, are a high-level parallel programming model for parallel and distributed computing.

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).

Programming with Big Data in R (pbdR) is a series of R packages and an environment for statistical computing with big data by using high-performance statistical computation. The pbdR uses the same programming language as R with S3/S4 classes and methods which is used among statisticians and data miners for developing statistical software. The significant difference between pbdR and R code is that pbdR mainly focuses on distributed memory systems, where data are distributed across several processors and analyzed in a batch mode, while communications between processors are based on MPI that is easily used in large high-performance computing (HPC) systems. R system mainly focuses on single multi-core machines for data analysis via an interactive mode such as GUI interface.

In computing, a memory access pattern or IO access pattern is the pattern with which a system or program reads and writes memory on secondary storage. These patterns differ in the level of locality of reference and drastically affect cache performance, and also have implications for the approach to parallelism and distribution of workload in shared memory systems. Further, cache coherency issues can affect multiprocessor performance, which means that certain memory access patterns place a ceiling on parallelism.

References

  1. Ebcioğlu, Kemal; Saraswat, Vijay; Sarkar, Vivek. "X10: Programming for Hierarchical Parallelism and NonUniform Data Access". CiteSeerX   10.1.1.135.9826 .
  2. Saraswat, Vijay; Bloom, Bard; Peshansky, Igor; Tardieu, Olivier; Grove, David (October 3, 2014). "X10 Language Specification Version 2.5" (PDF).
  3. Biever, C. "Computer revolution poses problems for programmers". New Scientist. 193 (2594).