11,997 research outputs found
Lost in Abstraction: Monotonicity in Multi-Threaded Programs (Extended Technical Report)
Monotonicity in concurrent systems stipulates that, in any global state,
extant system actions remain executable when new processes are added to the
state. This concept is not only natural and common in multi-threaded software,
but also useful: if every thread's memory is finite, monotonicity often
guarantees the decidability of safety property verification even when the
number of running threads is unknown. In this paper, we show that the act of
obtaining finite-data thread abstractions for model checking can be at odds
with monotonicity: Predicate-abstracting certain widely used monotone software
results in non-monotone multi-threaded Boolean programs - the monotonicity is
lost in the abstraction. As a result, well-established sound and complete
safety checking algorithms become inapplicable; in fact, safety checking turns
out to be undecidable for the obtained class of unbounded-thread Boolean
programs. We demonstrate how the abstract programs can be modified into
monotone ones, without affecting safety properties of the non-monotone
abstraction. This significantly improves earlier approaches of enforcing
monotonicity via overapproximations
The Paths to Choreography Extraction
Choreographies are global descriptions of interactions among concurrent
components, most notably used in the settings of verification (e.g., Multiparty
Session Types) and synthesis of correct-by-construction software (Choreographic
Programming). They require a top-down approach: programmers first write
choreographies, and then use them to verify or synthesize their programs.
However, most existing software does not come with choreographies yet, which
prevents their application.
To attack this problem, we propose a novel methodology (called choreography
extraction) that, given a set of programs or protocol specifications,
automatically constructs a choreography that describes their behavior. The key
to our extraction is identifying a set of paths in a graph that represents the
symbolic execution of the programs of interest. Our method improves on previous
work in several directions: we can now deal with programs that are equipped
with a state and internal computation capabilities; time complexity is
dramatically better; we capture programs that are correct but not necessarily
synchronizable, i.e., they work because they exploit asynchronous
communication
NASA Space Engineering Research Center for VLSI systems design
This annual review reports the center's activities and findings on very large scale integration (VLSI) systems design for 1990, including project status, financial support, publications, the NASA Space Engineering Research Center (SERC) Symposium on VLSI Design, research results, and outreach programs. Processor chips completed or under development are listed. Research results summarized include a design technique to harden complementary metal oxide semiconductors (CMOS) memory circuits against single event upset (SEU); improved circuit design procedures; and advances in computer aided design (CAD), communications, computer architectures, and reliability design. Also described is a high school teacher program that exposes teachers to the fundamentals of digital logic design
CPL: A Core Language for Cloud Computing -- Technical Report
Running distributed applications in the cloud involves deployment. That is,
distribution and configuration of application services and middleware
infrastructure. The considerable complexity of these tasks resulted in the
emergence of declarative JSON-based domain-specific deployment languages to
develop deployment programs. However, existing deployment programs unsafely
compose artifacts written in different languages, leading to bugs that are hard
to detect before run time. Furthermore, deployment languages do not provide
extension points for custom implementations of existing cloud services such as
application-specific load balancing policies.
To address these shortcomings, we propose CPL (Cloud Platform Language), a
statically-typed core language for programming both distributed applications as
well as their deployment on a cloud platform. In CPL, application services and
deployment programs interact through statically typed, extensible interfaces,
and an application can trigger further deployment at run time. We provide a
formal semantics of CPL and demonstrate that it enables type-safe, composable
and extensible libraries of service combinators, such as load balancing and
fault tolerance.Comment: Technical report accompanying the MODULARITY '16 submissio
Parallel processing and expert systems
Whether it be monitoring the thermal subsystem of Space Station Freedom, or controlling the navigation of the autonomous rover on Mars, NASA missions in the 1990s cannot enjoy an increased level of autonomy without the efficient implementation of expert systems. Merely increasing the computational speed of uniprocessors may not be able to guarantee that real-time demands are met for larger systems. Speedup via parallel processing must be pursued alongside the optimization of sequential implementations. Prototypes of parallel expert systems have been built at universities and industrial laboratories in the U.S. and Japan. The state-of-the-art research in progress related to parallel execution of expert systems is surveyed. The survey discusses multiprocessors for expert systems, parallel languages for symbolic computations, and mapping expert systems to multiprocessors. Results to date indicate that the parallelism achieved for these systems is small. The main reasons are (1) the body of knowledge applicable in any given situation and the amount of computation executed by each rule firing are small, (2) dividing the problem solving process into relatively independent partitions is difficult, and (3) implementation decisions that enable expert systems to be incrementally refined hamper compile-time optimization. In order to obtain greater speedups, data parallelism and application parallelism must be exploited
Arguing Machines: Human Supervision of Black Box AI Systems That Make Life-Critical Decisions
We consider the paradigm of a black box AI system that makes life-critical
decisions. We propose an "arguing machines" framework that pairs the primary AI
system with a secondary one that is independently trained to perform the same
task. We show that disagreement between the two systems, without any knowledge
of underlying system design or operation, is sufficient to arbitrarily improve
the accuracy of the overall decision pipeline given human supervision over
disagreements. We demonstrate this system in two applications: (1) an
illustrative example of image classification and (2) on large-scale real-world
semi-autonomous driving data. For the first application, we apply this
framework to image classification achieving a reduction from 8.0% to 2.8% top-5
error on ImageNet. For the second application, we apply this framework to Tesla
Autopilot and demonstrate the ability to predict 90.4% of system disengagements
that were labeled by human annotators as challenging and needing human
supervision
Acute: high-level programming language design for distributed computation
Existing languages provide good support for typeful programming of standalone programs. In a distributed system, however, there may be interaction between multiple instances of many distinct programs, sharing some (but not necessarily all) of their module structure, and with some instances rebuilt with new versions of certain modules as time goes on. In this paper we discuss programming language support for such systems, focussing on their typing and naming issues. We describe an experimental language, Acute, which extends an ML core to support distributed development, deployment, and execution, allowing type-safe interaction between separately-built programs. The main features are: (1) type-safe marshalling of arbitrary values; (2) type names that are generated (freshly and by hashing) to ensure that type equality tests suffice to protect the invariants of abstract types, across the entire distributed system; (3) expression-level names generated to ensure that name equality tests suffice for type-safety of associated values, e.g. values carried on named channels; (4) controlled dynamic rebinding of marshalled values to local resources; and (5) thunkification of threads and mutexes to support computation mobility. These features are a large part of what is needed for typeful distributed programming. They are a relatively lightweight extension of ML, should be efficiently implementable, and are expressive enough to enable a wide variety of distributed infrastructure layers to be written as simple library code above the byte-string network and persistent store APIs. This disentangles the language runtime from communication intricacies. This paper highlights the main design choices in Acute. It is supported by a full language definition (of typing, compilation, and operational semantics), by a prototype implementation, and by example distribution libraries
khmer: Working with Big Data in Bioinformatics
We introduce design and optimization considerations for the 'khmer' package.Comment: Invited chapter for forthcoming book on Performance of Open Source
Application
Automatic abstraction for synthesis and verification of deterministic timed systems
Journal ArticleThis paper presents a new approach for synthesis and verification of asynchronous circuits by using abstraction. It attacks the state explosion problem by avoiding the generation of a flat state space for the whole design. Instead, it breaks the design into sub-blocks and conducts synthesis and verification on each of them. Using this approach, the speed of synthesis and verification improves dramatically. This paper introduces how abstraction is applied to times Petri-nets to speed up synthesis and verification
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