Expression Acceleration: Seamless Parallelization of Typed High-Level Languages

Efficient parallelization of algorithms on general-purpose GPUs is today essential in many areas. However, it is a non-trivial task for software engineers to utilize GPUs to improve the performance of high-level programs in general. Although many domain-specific approaches are available for GPU acceleration, it is difficult to accelerate existing high-level programs without rewriting parts of the programs using low-level GPU code. In this paper, we propose a different approach, where expressions are marked for acceleration, and the compiler automatically infers which code needs to be accelerated. We call this approach expression acceleration. We design a compiler pipeline for the approach and show how to handle several challenges, including expression extraction, well-formedness, and compiling using multiple backends. The approach is designed and implemented within a statically-typed functional intermediate language and evaluated using three distinct non-trivial case studies

Generic level polymorphic N-ary functions

Agda's standard library struggles in various places with n-ary functions and relations. It introduces congruence and substitution operators for functions of arities one and two, and provides users with convenient combinators for manipulating indexed families of arity exactly one. After a careful analysis of the kinds of problems the unifier can easily solve, we design a unifier-friendly representation of n-ary functions. This allows us to write generic programs acting on n-ary functions which automatically reconstruct the representation of their inputs' types by unification. In particular, we can define fully level polymorphic n-ary versions of congruence, substitution and the combinators for indexed families, all requiring minimal user input

First-class models: on a noncausal language for higher-order and structurally dynamic modelling and simulation

The field of physical modelling and simulation plays a vital role in advancing numerous scientific and engineering disciplines. To cope with the increasing size and complexity of physical models, a number of modelling and simulation languages have been developed. These languages can be divided into two broad categories: causal and noncausal. Causal languages express a system model in terms of directed equations. In contrast, a noncausal model is formulated in terms of undirected equations. The fact that the causality can be left implicit makes noncausal languages more declarative and noncausal models more reusable. These are considered to be crucial advantages in many physical domains. Current, mainstream noncausal languages do not treat equational models as first-class values; that is, a model cannot be parametrised on other models or generated at simulation runtime. This results in very limited higher-order and structurally dynamic modelling capabilities, and limits the expressiveness and applicability of noncausal languages. This thesis is about a novel approach to the design and implementation of noncausal languages with first-class models supporting higher-order and structurally dynamic modelling. In particular, the thesis presents a language that enables: (1) higher-order modelling capabilities by embedding noncausal models as first-class entities into a functional programming language and (2) efficient simulation of noncausal models that are generated at simulation runtime by runtime symbolic processing and just-in-time compilation. These language design and implementation approaches can be applied to other noncausal languages. This thesis provides a self-contained reference for such an undertaking by defining the language semantics formally and providing an in-depth description of the implementation. The language provides noncausal modelling and simulation capabilities that go beyond the state of the art, as backed up by a range of examples presented in the thesis, and represents a significant progress in the field of physical modelling and simulation

First-class models: on a noncausal language for higher-order and structurally dynamic modelling and simulation

The field of physical modelling and simulation plays a vital role in advancing numerous scientific and engineering disciplines. To cope with the increasing size and complexity of physical models, a number of modelling and simulation languages have been developed. These languages can be divided into two broad categories: causal and noncausal. Causal languages express a system model in terms of directed equations. In contrast, a noncausal model is formulated in terms of undirected equations. The fact that the causality can be left implicit makes noncausal languages more declarative and noncausal models more reusable. These are considered to be crucial advantages in many physical domains. Current, mainstream noncausal languages do not treat equational models as first-class values; that is, a model cannot be parametrised on other models or generated at simulation runtime. This results in very limited higher-order and structurally dynamic modelling capabilities, and limits the expressiveness and applicability of noncausal languages. This thesis is about a novel approach to the design and implementation of noncausal languages with first-class models supporting higher-order and structurally dynamic modelling. In particular, the thesis presents a language that enables: (1) higher-order modelling capabilities by embedding noncausal models as first-class entities into a functional programming language and (2) efficient simulation of noncausal models that are generated at simulation runtime by runtime symbolic processing and just-in-time compilation. These language design and implementation approaches can be applied to other noncausal languages. This thesis provides a self-contained reference for such an undertaking by defining the language semantics formally and providing an in-depth description of the implementation. The language provides noncausal modelling and simulation capabilities that go beyond the state of the art, as backed up by a range of examples presented in the thesis, and represents a significant progress in the field of physical modelling and simulation

RA-LPEL: A Resource-Aware Light-Weight Parallel Execution Layer for Reactive Stream Processing Networks on The SCC Many-core Tiled Architecture

In computing the available computing power has continuously fallen short of the demanded computing performance. As a consequence, performance improvement has been the main focus of processor design. However, due to the phenomenon called “Power Wall” it has become infeasible to build faster processors by just increasing the processor’s clock speed. One of the resulting trends in hardware design is to integrate several simple and power-efficient cores on the same chip. This design shift poses challenges of its own. In the past, with increasing clock frequency the programs became automatically faster as well without modifications. This is no longer true with many-core architectures. To achieve maximum performance the programs have to run concurrently on more than one core, which forces the general computing paradigm to become increasingly parallel to leverage maximum processing power. In this thesis, we will focus on the Reactive Stream Program (RSP). In stream processing, the system consists of computing nodes, which are connected via communication streams. These streams simplify the concurrency management on modern many-core architectures due to their implicit synchronisation. RSP is a stream processing system that implements the reactive system. The RSPs work in tandem with their environment and the load imposed by the environment may vary over time. This provides a unique opportunity to increase performance per watt. In this thesis the research contribution focuses on the design of the execution layer to run RSPs on tiled many-core architectures, using the Intel’s Single-chip Cloud Computer (SCC) processor as a concrete experimentation platform. Further, we have developed a Dynamic Voltage and Frequency Scaling (DVFS) technique for RSP deployed on many-core architectures. In contrast to many other approaches, our DVFS technique does not require the capability of controlling the power settings of individual computing elements, thus making it applicable for modern many-core architectures, with which power can be changed only for power islands. The experimental results confirm that the proposed DVFS technique can effectively improve the energy efficiency, i.e. increase the performance per watt, for RSPs

Annual Report of the University, 2005-2006, Volumes 1-7

PROPOSED POLICIES The Office of Government & Community Relations is in charge of advancing the University\u27s interests at all levels of federal, state and local government. The following policy guidelines for working with University units will achieve a coordinated and effective institutional advancement program. • To inform the Office of Government & Community Relations of all planned contacts and correspondence with elected officials and policy-making employees of federal, state and local government, including those who are alumni or friends of the University. Those items which pertain to sponsored research should be coordinated with the Vice President for Research. • To consult the Office of Government & Community Relations on any verbal or written statements made on behalf of the University that concern federal, state or local policies, legislation or regulations. • To advise the Office of Government & Community Relations on any activities, conferences, seminars, lectures or projects that involve the community and/or impact the University area. • Faculty or staff members who contact federal, state or local policy-making employees as experts in a specific field, or who act on behalf of themselves or another organization, should include a disclaimer which clearly states that they are not acting on behalf of the University

A formal framework for heterogeneous systems semantics

Cyber physical systems are usually complex systems which are often critical, meaning their failure can have significant negative impacts on human lives. A key point in their development is the verification and validation (V & V) activities which are used to assess their correctness towards user requirements and the associated specifications. This process aims at avoiding failure cases, thus preventing any incident or accident. In order to conduct these V & V steps on such complex systems, separations of concerns of various nature are used. In that purpose, the system is modeled using heterogeneous models that have to be combined together. The nature of these separations of concerns can be as follows: horizontal, which corresponds to a structural decomposition of the system; vertical, which corresponds to the different steps leading from the abstract specification to the concrete implementation; and transversal, which consists in gathering together the parts that are thematically identical (function, performance, security, safety...). These parts are usually expressed using domain specific modeling languages, while the V & V activities are historically conducted using testing and proofreading, and more and more often, using formal methods, which is advocated in our approach. In all these cases, the V & V activities must take into account these separations in order to provide confidence in the global system from the confidence of its sub-parts bound to the separation in question. In other words, to ensure the correctness of the system, a behavioral semantics is needed which has to rely on the ad-hoc semantics of the subsystems. In order to define it, these semantics must be successfully combined in a single formalism. This thesis stems from the GEMOC project a workbench that allows the definition of various languages along with their coordination properties, and target the formal modeling of the GEMOC core through the association of trace semantics to each preoccupation and the expression of constraints between them to encode the correct behavior of the system. This thesis follows several other works conducted under the TOPCASED, OPEES, QuarteFt, P and GEMOC projects, and provides four contributions in that global context: the first one proposes a methodology to give an operational semantics to executable models illustrated through two case studies: Petri nets and models of processes. The second one proposes a formal context on which refinement can be expressed to tackle vertical separation. The third one gives a denotational semantics to CCSL which is the language that is currently used in the GEMOC projects to express behavioural properties between events from one or several models, possibly heterogeneous. Finally, the fourth one proposes an investigation on how to extend CCSL with the notion of refinement we proposed. All these contribution are mechanized in the Agda proof assistant, and thus have been modeled and proven in a formal manner

The Pandemic A Leap Of Faith - Covid-19 Vaccine Fatwa in Indonesia Religious Institutions Independence and Rival Politics

Imagine where we are in 2019. We are still living in best possible way, we gather, we socialize, and we celebrate plenty of things together with our loved one without worry. The 2019 is just two years ago, but in certainly feels like longer than that. Now, we are at the year of 2021. The pandemic has beeb with us for 17 months now. The countres all over the worlds loosen and tighten its boreder as the pandemics evolve into certainty when the vaccinations held. Indees, the catastrophic of the pandemics didn't just leave us behind, many of us losing out loved one and in grief. Yet we are still hopeful of the future especialyy when Science nurtured our thinking while God is with all of us at heart