There are billions of lines of sequential code inside nowadays' software which do not benefit from the parallelism available in modern multicore architectures. Automatically parallelizing sequential code, to promote an efficient use of the available parallelism, has been a research goal for some time now. This work proposes a new approach for achieving such goal. We created a new parallelizing compiler that analyses the read and write instructions, and control-flow modifications in programs to identify a set of dependencies between the instructions in the program. Afterwards, the compiler, based on the generated dependencies graph, rewrites and organizes the program in a task-oriented structure. Parallel tasks are composed by instructions that cannot be executed in parallel. A work-stealing-based parallel runtime is responsible for scheduling and managing the granularity of the generated tasks. Furthermore, a compile-time granularity control mechanism also avoids creating unnecessary data-structures. This work focuses on the Java language, but the techniques are general enough to be applied to other programming languages. We have evaluated our approach on 8 benchmark programs against OoOJava, achieving higher speedups. In some cases, values were close to those of a manual parallelization. The resulting parallel code also has the advantage of being readable and easily configured to improve further its performance manually.
Given the wide availability of multicore processors, GPUs and other accelerators such as FPGAs and the Xeon Phi, research on concurrent programming has increased in the last decade. New programming models, languages and runtime systems have been developed to improve the expression and execution of parallel programs. Much of this work has culminated in new languages, such as X10 @cite_13 , Fortress @cite_0 and Chapel @cite_32 , in which most language constructs are default by parallel (such as for cycles, for instance). These languages also provide constructs to explicitly inform the compiler that certain memory regions are independent and, therefore, accesses to them can be executed in parallel. Unlike these languages, which mostly target scientific computing, the Æminium language @cite_19 has focused on dependable systems programming. By annotating variables with access permissions, programs could be automatically parallelized with guarantees that the execution would not break the defined contracts.
@cite_0 @cite_19 @cite_13 @cite_32
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Summary form only given. The Programming Language Research Group at Sun Microsystems Laboratories seeks to apply lessons learned from the Java (TM) programming language to the next generation of programming languages. The Java language supports platform-independent parallel programming with explicit multithreading and explicit locks. As part of the DARPA program for High Productivity Computing Systems, we are developing Fortress, a language intended to support large-scale scientific computation. One of the design principles is that parallelism be encouraged everywhere (for example, it is intentionally just a little bit harder to write a sequential loop than a parallel loop). Another is to have fairly rich mechanisms for encapsulation and abstraction; the idea is to have a fairly complicated language for library writers that enable them to write libraries that present a relatively simple set of interfaces to the application programmer. We will discuss ideas for using a rich polymorphic type system to organize multithreading and data distribution on large parallel machines. The net result is similar in some ways to data distribution facilities in other languages such as HPF and Chapel, but more open-ended, because in Fortress the facilities are defined by user-replaceable libraries rather than wired into the compiler. Writing concurrent applications is extremely challenging, not only in terms of producing bug-free and maintainable software, but also for enabling developer productivity. In this article we present the AEminium concurrent-by-default programming language. Using AEminium programmers express data dependencies rather than control flow between instructions. Dependencies are expressed using permissions, which are used by the type system to automatically parallelize the application. The AEminium approach provides a modular and composable mechanism for writing concurrent applications, preventing data races in a provable way. This allows programmers to shift their attention from low-level, error-prone reasoning about thread interleaving and synchronization to focus on the core functionality of their applications. We study the semantics of AEminium through μAEminium, a sound core calculus that leverages permission flow to enable concurrent-by-default execution. After discussing our prototype implementation we present several case studies of our system. Our case studies show up to 6.5X speedup on an eight-core machine when leveraging data group permissions to manage access to shared state, and more than 70p higher throughput in a Web server application. 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™ languages. In this paper we consider productivity challenges for parallel programmers and explore ways that parallel language design might help improve end-user productivity. We offer a candidate list of desirable qualities for a parallel programming language, and describe how these qualities are addressed in the design of the Chapel language. In doing so, we provide an overview of Chapel's features and how they help address parallel productivity. We also survey current techniques for parallel programming and describe ways in which we consider them to fall short of our idealized productive programming model.