X10

Abstract

It is now well established that the device scaling predicted by Moore's Law is no longer a viable option for increasing the clock frequency of future uniprocessor systems at the rate that had been sustained during the last two decades. As a result, future systems are rapidly moving from uniprocessor to multiprocessor configurations, so as to use parallelism instead of frequency scaling as the foundation for increased compute capacity. The dominant emerging multiprocessor structure for the future is a Non-Uniform Cluster Computing (NUCC) system with nodes that are built out of multi-core SMP chips with non-uniform memory hierarchies, and interconnected in horizontally scalable cluster configurations such as blade servers. Unlike previous generations of hardware evolution, this shift will have a major impact on existing software. Current OO language facilities for concurrent and distributed programming are inadequate for addressing the needs of NUCC systems because they do not support the notions of non-uniform data access within a node, or of tight coupling of distributed nodes.We have designed a modern object-oriented programming language, X10, for high performance, high productivity programming of NUCC systems. A member of the partitioned global address space family of languages, X10 highlights the explicit reification of locality in the form of places; lightweight activities embodied in async, future, foreach, and ateach constructs; a construct for termination detection (finish); the use of lock-free synchronization (atomic blocks); and the manipulation of cluster-wide global data structures. We present an overview of the X10 programming model and language, experience with our reference implementation, and results from some initial productivity comparisons between the X10 and Java TM languages.

A taxonomy of task-based parallel programming technologies for high-performance computing

“…In this context, tasks are often combined with a global address space (GAS) programming model such as GASPI [25] and scheduled across multiple processes, which together form the distributed execution of a single task-parallel program. While some examples of global address space environments with task-based parallelism are specifically designed languages such as Chapel [7] and X10 [8], it is also possible to implement these concepts as a library. For instance, HPX [17] and Charm++ [18] are asynchronous GAS runtimes.…”

Using utility prediction models to dynamically choose program thread counts

“…CPU allocation may be done with or without the aid of application knowledge. Application knowledge may be embedded in the source code (potentially extractable later on) [8]- [11]. Knowledge may also come in the form of programmer inserted hints [12] or gathered at runtime, or via profiling [13]- [15].…”

PGAS for Distributed Numerical Python Targeting Multi-core Clusters

“…Many recent parallel programming languages, such as UPC, Co-array Fortran, X10 and Chapel [14], [15], [16], [17], [18], [19], [20], provide the feature of Partitioned Global Address Space (PGAS), which combines the efficiency of leveraging data locality and the programming productivity of using shared-memory. Our programming system based on Python belongs to the PGAS family and supports global shared data across all processes and private local data within each process.…”