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Register a new userLiquidsoap: a High-Level Programming Language for Multimedia Streaming
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Added by
Samuel Mimram
Publication date
2011
fields
Informatics Engineering
and research's language is
English
Authors
David Baelde
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Generating multimedia streams, such as in a netradio, is a task which is complex and difficult to adapt to every users needs. We introduce a novel approach in order to achieve it, based on a dedicated high-level functional programming language, called Liquidsoap, for generating, manipulating and broadcasting multimedia streams. Unlike traditional approaches, which are based on configuration files or static graphical interfaces, it also allows the user to build complex and highly customized systems. This language is based on a model for streams and contains operators and constructions, which make it adapted to the generation of streams. The interpreter of the language also ensures many properties concerning the good execution of the stream generation.
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Quantum chromodynamics (QCD) is the theory of subnuclear physics, aiming at mod- eling the strong nuclear force, which is responsible for the interactions of nuclear particles. Lattice QCD (LQCD) is the corresponding discrete formulation, widely used for simula- tions. The computational demand for the LQCD is tremendous. It has played a role in the history of supercomputers, and has also helped defining their future. Designing efficient LQCD codes that scale well on large (probably hybrid) supercomputers requires to express many levels of parallelism, and then to explore different algorithmic solutions. While al- gorithmic exploration is the key for efficient parallel codes, the process is hampered by the necessary coding effort. We present in this paper a domain-specific language, QIRAL, for a high level expression of parallel algorithms in LQCD. Parallelism is expressed through the mathematical struc- ture of the sparse matrices defining the problem. We show that from these expressions and from algorithmic and preconditioning formulations, a parallel code can be automatically generated. This separates algorithms and mathematical formulations for LQCD (that be- long to the field of physics) from the effective orchestration of parallelism, mainly related to compilation and optimization for parallel architectures.
Algorithmic and data refinement are well studied topics that provide a mathematically rigorous approach to gradually introducing details in the implementation of software. Program refinements are performed in the context of some programming language, but mainstream languages lack features for recording the sequence of refinement steps in the program text. To experiment with the combination of refinement, automated verification, and language design, refinement features have been added to the verification-aware programming language Dafny. This paper describes those features and reflects on some initial usage thereof.
Multitier programming languages reduce the complexity of developing distributed systems by developing the distributed system in a single coherent code base. The compiler or the runtime separate the code for the components of the distributed system, enabling abstraction over low level implementation details such as data representation, serialization and network protocols. Our ScalaLoci language allows developers to declare the different components and their architectural relation at the type level, allowing static reasoning about about distribution and remote communication and guaranteeing static type safety across components. The compiler splits the multitier program into the component-specific code and automatically generates the communication boilerplate. Communication between components can be modeled by declaratively specifying data flows between components using reactive programming. In this paper, we report on the implementation of our design and our experience with embedding our language features into Scala as a host language. We show how a combination of Scalas advanced type level programming and its macro system can be used to enrich the language with new abstractions. We comment on the challenges we encountered and the solutions we developed for our current implementation and outline suggestions for an improved macro system to support the such use cases of embedding of domain-specific abstractions.
While modern software development heavily uses versioned packages, programming languages rarely support the concept
Though statistical analyses are centered on research questions and hypotheses, current statistical analysis tools are not. Users must first translate their hypotheses into specific statistical tests and then perform API calls with functions and parameters. To do so accurately requires that users have statistical expertise. To lower this barrier to valid, replicable statistical analysis, we introduce Tea, a high-level declarative language and runtime system. In Tea, users express their study design, any parametric assumptions, and their hypotheses. Tea compiles these high-level specifications into a constraint satisfaction problem that determines the set of valid statistical tests, and then executes them to test the hypothesis. We evaluate Tea using a suite of statistical analyses drawn from popular tutorials. We show that Tea generally matches the choices of experts while automatically switching to non-parametric tests when parametric assumptions are not met. We simulate the effect of mistakes made by non-expert users and show that Tea automatically avoids both false negatives and false positives that could be produced by the application of incorrect statistical tests.
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