Aspect-oriented programming (AOP) is a technique for improving separation of concerns in software design and implementation. AOP works by providing explicit mechanisms for capturing the structure of crosscutting concerns. This tutorial shows how to use AOP to implement crosscutting conerns in a concise modular way. It works with AspectJ, a seamless aspect-oriented extension to the Java(tm) programming language, and with AspectC, an aspect-oriented extension to C in the style of AspectJ. It also includes a description of their underlying model, in terms of which a wide range of AOP languages can be understood.

Aspect-oriented programming

Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

AspectJ? is a simple and practical aspect-oriented extension to Java?. With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

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An Overview of AspectJ

Transactional Memory (TM) is emerging as a promising technology to simplify parallel programming. While several TM systems have been proposed in the research literature, we are still missing the tools and workloads necessary to analyze and compare the proposals. Most TM systems have been evaluated using microbenchmarks, which may not be representative of any real-world behavior, or individual applications, which do not stress a wide range of execution scenarios. We introduce the Stanford Transactional Application for Multi-Processing (STAMP), a comprehensive benchmark suite for evaluating TM systems. STAMP includes eight applications and thirty variants of input parameters and data sets in order to represent several application domains and cover a wide range of transactional execution cases (frequent or rare use of transactions, large or small transactions, high or low contention, etc.). Moreover, STAMP is portable across many types of TM systems, including hardware, software, and hybrid systems. In this paper, we provide descriptions and a detailed characterization of the applications in STAMP. We also use the suite to evaluate six different TM systems, identify their shortcomings, and motivate further research on their performance characteristics.

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STAMP: Stanford Transactional Applications for Multi-Processing

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.

/pdf/parallel-programmability-and-the-chapel-language-3fa3iypnkj.pdf

Parallel Programmability and the Chapel Language

To provide, generally, systems, devices, methods and computer readable media for implementing function callback requests between a first processor (e.g., a GPU) and a second processor (e.g., a CPU).SOLUTION: The system may include a shared virtual memory (SVM) coupled to the first and second processors, the SVM configured to store at least one double-ended queue (Deque). An execution unit (EU) of the first processor may be associated with a first of the Deques and configured to push the callback requests to the first Deque. A request handler thread executing on the second processor may be configured to: pop one of the callback requests from the first Deque; execute a function specified by the popped callback request; and generate a completion signal to the EU in response to completion of the function.SELECTED DRAWING: Figure 1

Improved function callback mechanism between central processing unit (cpu) and auxiliary processor

Parallel hardware is today's reality and parallel programming models exist for most mainstream languages. Surprisingly JavaScript, the mother tongue of the web, is still stuck in its sequential past. JavaScript's unique programming model, the web's safety and security requirements, and the expectations of its programmers for determinism have impeded parallelization until now.Parallel JavaScript (code named River Trail) is a set of JavaScript extensions and an API that provides safe, deterministic parallelism to web applications while fitting within the event-driven, security oriented world of JavaScript applications. River Trail allows programmers to utilize available hardware parallelism on client machines - from SIMD units to multiple cores, using high-level parallel programming patterns.In this paper we describe River Trail, show how it can be used to provide a gentle introduction to parallelism using our selfcontained hands-on tutorial. At the end of the tutorial the student will have mastered the basic concepts of parallel programming and use this knowledge to build a realistic parallel HTML5 web application in River Trail.

Teaching parallelism with river trail

One embodiment provides for a compute apparatus to perform machine learning operations, the compute apparatus comprising instruction decode logic to decode a single instruction including multiple operands into a single decoded instruction, the multiple operands having differing precisions and a general-purpose graphics compute unit including a first logic unit and a second logic unit, the general-purpose graphics compute unit to execute the single decoded instruction, wherein to execute the single decoded instruction includes to perform a first instruction operation on a first set of operands of the multiple operands at a first precision and a simultaneously perform second instruction operation on a second set of operands of the multiple operands at a second precision.

Instructions having support for floating point and integer data types in the same register

Techniques for improved transactional memory management are described. In one embodiment, for example, an apparatus may comprise a processor element, an execution component for execution by the processor element to concurrently execute a software transaction and a hardware transaction according to a transactional memory process, a tracking component for execution by the processor element to activate a global lock to indicate that the software transaction is undergoing execution, and a finalization component for execution by the processor element to commit the software transaction and deactivate the global lock when execution of the software transaction completes, the finalization component to abort the hardware transaction when the global lock is active when execution of the hardware transaction completes. Other embodiments are described and claimed.

Tatiana Shpeisman

Papers

Improved function callback mechanism between central processing unit (cpu) and auxiliary processor

Teaching parallelism with river trail

Instructions having support for floating point and integer data types in the same register

Improved transactional memory management techniques

Unbegrenzter Transaktionsspeicher mit Forward-Progress-Garantien unter Verwendung eines globalen Hardware-Schlosses