Proceedings of the 3rd Workshop on Domain-Specific Language Design and Implementation (DSLDI 2015)

The goal of the DSLDI workshop is to bring together researchers and practitioners interested in sharing ideas on how DSLs should be designed, implemented, supported by tools, and applied in realistic application contexts. We are both interested in discovering how already known domains such as graph processing or machine learning can be best supported by DSLs, but also in exploring new domains that could be targeted by DSLs. More generally, we are interested in building a community that can drive forward the development of modern DSLs. These informal post-proceedings contain the submitted talk abstracts to the 3rd DSLDI workshop (DSLDI'15), and a summary of the panel discussion on Language Composition

The post office experience: designing a large asynchronous chip

Journal ArticleThe Post Office is an asynchronous, 300,000 transistor, full-custom CMOS chip designed as the communication component for the Mayfly scalable parallel processor. Performance requirements led to the development of a design style which permits the design of sequential circuits operating under a restricted form of multiple input change sign alling called burst-mode. The Post Office complexity forced us to develop a set of design fools capable of correctly synthesizing transistor circuits front state machine and equation specifications, and capable of verifying the correctness of the resultant circuity using implementation specific timing assumptions. The paper provides a case study of this design experience

Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

Simulation of networks of spiking neurons: A review of tools and strategies

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of Computational Neuroscience, in press (2007

Proceedings of the 3rd Workshop on Domain-Specific Language Design and Implementation (DSLDI'15)

The goal of the DSLDI workshop is to bring together researchers and practitioners interested in sharing ideas on how DSLs should be designed, implemented, supported by tools, and applied in realistic application contexts. We are both interested in discovering how already known domains such as graph processing or machine learning can be best supported by DSLs, but also in exploring new domains that could be targeted by DSLs. More generally, we are interested in building a community that can drive forward the development of modern DSLs. These informal post-proceedings contain the submitted talk abstracts to the 3rd DSLDI workshop (DSLDI'15), and a summary of the panel discussion on Language Composition

Structured Composition of Dataflow and Control-Flow for Reusable and Robust Scientific Workflows

Data-centric scientific workflows are often modeled as dataflow process networks. The simplicity of the dataflow framework facilitates workflow design, analysis, and optimization. However, some workflow tasks are particularly ''control-flow intensive'', e.g., procedures to make workflows more fault-tolerant and adaptive in an unreliable, distributed computing environment. Modeling complex control-flow directly within a dataflow framework often leads to overly complicated workflows that are hard to comprehend, reuse, schedule, and maintain. In this paper, we develop a framework that allows a structured embedding of control-flow intensive subtasks within dataflow process networks. In this way, we can seamlessly handle complex control-flows without sacrificing the benefits of dataflow. We build upon a flexible actor-oriented modeling and design approach and extend it with (actor) frames and (workflow) templates. A frame is a placeholder for an (existing or planned) collection of components with similar function and signature. A template partially specifies the behavior of a subworkflow by leaving ''holes'' (i.e., frames) in the subworkflow definition. Taken together, these abstraction mechanisms facilitate the separation and structured re-combination of control-flow and dataflow in scientific workflow applications. We illustrate our approach with a real-world scientific workflow from the astrophysics domain. This data-intensive workflow requires remote execution and file transfer in a semi-reliable environment. For such work-flows, we propose a 3-layered architecture: The top-level, typically a dataflow process network, includes Generic Data Transfer (GDT) frames and Generic remote eXecution (GX) frames. At the second level, the user can specialize the behavior of these generic components by embedding a suitable template (here: transducer templates for control-flow intensive tasks). At the third level, frames inside the transducer template are specialized by embedding the desired implementation. Our approach yields workflows that are more robust (fault-tolerance strategies can be define by control-flow driven transducer templates) and at the same time more reuseable, since the embedding of frames and templates yields more structured and modular workflows