18,548 research outputs found
Supporting shared data structures on distributed memory architectures
Programming nonshared memory systems is more difficult than programming shared memory systems, since there is no support for shared data structures. Current programming languages for distributed memory architectures force the user to decompose all data structures into separate pieces, with each piece owned by one of the processors in the machine, and with all communication explicitly specified by low-level message-passing primitives. A new programming environment is presented for distributed memory architectures, providing a global name space and allowing direct access to remote parts of data values. The analysis and program transformations required to implement this environment are described, and the efficiency of the resulting code on the NCUBE/7 and IPSC/2 hypercubes are described
Compiling global name-space programs for distributed execution
Distributed memory machines do not provide hardware support for a global address space. Thus programmers are forced to partition the data across the memories of the architecture and use explicit message passing to communicate data between processors. The compiler support required to allow programmers to express their algorithms using a global name-space is examined. A general method is presented for analysis of a high level source program and along with its translation to a set of independently executing tasks communicating via messages. If the compiler has enough information, this translation can be carried out at compile-time. Otherwise run-time code is generated to implement the required data movement. The analysis required in both situations is described and the performance of the generated code on the Intel iPSC/2 is presented
The paradigm compiler: Mapping a functional language for the connection machine
The Paradigm Compiler implements a new approach to compiling programs written in high level languages for execution on highly parallel computers. The general approach is to identify the principal data structures constructed by the program and to map these structures onto the processing elements of the target machine. The mapping is chosen to maximize performance as determined through compile time global analysis of the source program. The source language is Sisal, a functional language designed for scientific computations, and the target language is Paris, the published low level interface to the Connection Machine. The data structures considered are multidimensional arrays whose dimensions are known at compile time. Computations that build such arrays usually offer opportunities for highly parallel execution; they are data parallel. The Connection Machine is an attractive target for these computations, and the parallel for construct of the Sisal language is a convenient high level notation for data parallel algorithms. The principles and organization of the Paradigm Compiler are discussed
A Refinement Calculus for Logic Programs
Existing refinement calculi provide frameworks for the stepwise development
of imperative programs from specifications. This paper presents a refinement
calculus for deriving logic programs. The calculus contains a wide-spectrum
logic programming language, including executable constructs such as sequential
conjunction, disjunction, and existential quantification, as well as
specification constructs such as general predicates, assumptions and universal
quantification. A declarative semantics is defined for this wide-spectrum
language based on executions. Executions are partial functions from states to
states, where a state is represented as a set of bindings. The semantics is
used to define the meaning of programs and specifications, including parameters
and recursion. To complete the calculus, a notion of correctness-preserving
refinement over programs in the wide-spectrum language is defined and
refinement laws for developing programs are introduced. The refinement calculus
is illustrated using example derivations and prototype tool support is
discussed.Comment: 36 pages, 3 figures. To be published in Theory and Practice of Logic
Programming (TPLP
Macroservers: An Execution Model for DRAM Processor-In-Memory Arrays
The emergence of semiconductor fabrication technology allowing a tight coupling between high-density DRAM and CMOS logic on the same chip has led to the important new class of Processor-In-Memory (PIM) architectures. Newer developments provide powerful parallel processing capabilities on the chip, exploiting the facility to load wide words in single memory accesses and supporting complex address manipulations in the memory. Furthermore, large arrays of PIMs can be arranged into a massively parallel architecture. In this report, we describe an object-based programming model based on the notion of a macroserver. Macroservers encapsulate a set of variables and methods; threads, spawned by the activation of methods, operate asynchronously on the variables' state space. Data distributions provide a mechanism for mapping large data structures across the memory region of a macroserver, while work distributions allow explicit control of bindings between threads and data. Both data and work distributuions are first-class objects of the model, supporting the dynamic management of data and threads in memory. This offers the flexibility required for fully exploiting the processing power and memory bandwidth of a PIM array, in particular for irregular and adaptive applications. Thread synchronization is based on atomic methods, condition variables, and futures. A special type of lightweight macroserver allows the formulation of flexible scheduling strategies for the access to resources, using a monitor-like mechanism
The Automatic Inference of State Invariants in TIM
As planning is applied to larger and richer domains the effort involved in
constructing domain descriptions increases and becomes a significant burden on
the human application designer. If general planners are to be applied
successfully to large and complex domains it is necessary to provide the domain
designer with some assistance in building correctly encoded domains. One way of
doing this is to provide domain-independent techniques for extracting, from a
domain description, knowledge that is implicit in that description and that can
assist domain designers in debugging domain descriptions. This knowledge can
also be exploited to improve the performance of planners: several researchers
have explored the potential of state invariants in speeding up the performance
of domain-independent planners. In this paper we describe a process by which
state invariants can be extracted from the automatically inferred type
structure of a domain. These techniques are being developed for exploitation by
STAN, a Graphplan based planner that employs state analysis techniques to
enhance its performance
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