Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userQIRAL: A High Level Language for Lattice QCD Code Generation
212
0
0.0
(
0
)
Added by
Denis Barthou
Publication date
2012
fields
Informatics Engineering
and research's language is
English
Authors
Denis Barthou
Ask ChatGPT about the research
No Arabic abstract
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.
rate research
Read More
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.
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.
This paper describes the design and implementation of CRAQL (Composable Repository Analysis and Query Language), a new query language for source code. The growth of source code mining and its applications suggest the need for a query language that can fully utilize and correlate across the unique structure and metadata of parsed source code. CRAQL is built on an underlying abstraction analogous to the underpinnings of SQL, but aimed at parsed source code. Thus, while SQL queries inputs and outputs are sets of tuples, CRAQL queries inputs and outputs are sets of abstract syntax trees (ASTs). This abstraction makes CRAQL queries composable (the output of one query can become the input to another) and improves the power of the language by allowing for querying of the tree structure and metadata, as well as raw text. Furthermore, the abstraction enables tree-specific language optimizations and allows CRAQL to be easily applied to any language that is parsable into ASTs. These attributes, along with a familiar syntax similar to SQL, allow complex queries to be expressed in a compact, straightforward manner. Questions such as find the longest series of statements without any loops, find methods that are never called, find getters (0-parameter methods with a single statement that returns a member variable), or find the percentage of variables declared at the top of a block all translate to simple, understandable queries in CRAQL. In this paper we describe the language, its features and capabilities. We compare CRAQL to other languages for querying source code and find that it has potential advantages in clarity and compactness. We discuss the features and optimizations added to support searching parse tree collections effectively and efficiently. Finally, we summarize the application of the language to millions of Java source files, the details of which are in a companion paper.
Backtracking (i.e., reverse execution) helps the user of a debugger to naturally think backwards along the execution path of a program, and thinking backwards makes it easy to locate the origin of a bug. So far backtracking has been implemented mostly by state saving or by checkpointing. These implementations, however, inherently do not scale. Meanwhile, a more recent backtracking method based on reverse-code generation seems promising because executing reverse code can restore the previous states of a program without state saving. In the literature, there can be found two methods that generate reverse code: (a) static reverse-code generation that pre-generates reverse code through static analysis before starting a debugging session, and (b) dynamic reverse-code generation that generates reverse code by applying dynamic analysis on the fly during a debugging session. In particular, we espoused the latter one in our previous work to accommodate non-determinism of a program caused by e.g., multi-threading. To demonstrate the usefulness of our dynamic reverse-code generation, this article presents a case study of various backtracking methods including ours. We compare the memory usage of various backtracking methods in a simple but nontrivial example, a bounded-buffer program. In the case of non-deterministic programs such as this bounded-buffer program, our dynamic reverse-code generation outperforms the existing backtracking methods in terms of memory efficiency.
Analyzing Ethereum bytecode, rather than the source code from which it was generated, is a necessity when: (1) the source code is not available (e.g., the blockchain only stores the bytecode), (2) the information to be gathered in the analysis is only visible at the level of bytecode (e.g., gas consumption is specified at the level of EVM instructions), (3) the analysis results may be affected by optimizations performed by the compiler (thus the analysis should be done ideally after compilation). This paper presents EthIR, a framework for analyzing Ethereum bytecode, which relies on (an extension of) OYENTE, a tool that generates CFGs; EthIR produces from the CFGs, a rule-based representation (RBR) of the bytecode that enables the application of (existing) high-level analyses to infer properties of EVM code.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
