Java 7 concurrency. Hardware trends drive programming idioms Languages, libraries, and frameworks shape the way we write programs. Even though Alonzo Church showed in 1934 that all the known computational frameworks were equivalent in the set of programs they could represent, the set of programs that real programmers actually write is shaped by the idioms that the programming model — driven by languages, libraries, and frameworks — makes easy to express. In turn, the dominant hardware platforms of the day shape the way we create languages, libraries, and frameworks. The Java language has had support for threads and concurrency from day 1; the language includes synchronization primitives such as synchronized and volatile, and the class library includes classes such as Thread. Going forward, the hardware trend is clear; Moore's Law will not be delivering higher clock rates, but instead delivering more cores per chip.
Exposing finer-grained parallelism Divide and conquer Listing 1. Fork-join Listing 2. Table 1. ParallelProcessing. A number of Python-related libraries exist for the programming of solutions either employing multiple CPUs or multicore CPUs in a symmetric multiprocessing (SMP) or shared memory environment, or potentially huge numbers of computers in a cluster or grid environment. This page seeks to provide references to the different libraries and solutions available. Just In Time Compilation Some Python libraries allow compiling Python functions at run time, this is called Just In Time (JIT) compilation.
Nuitka - As the authors say: Nuitka is a Python compiler written in Python ! You feed Nuitka your Python app, it does a lot of clever things, and spits out an executable or extension module. Nuitka translates Python into a C program that then is linked against libpython to execute exactly like CPython For version 0.6 of Nuitka and Python 2.7 speedup was 312% ! Symmetric Multiprocessing Advantages of such approaches include convenient process creation and the ability to share resources. Cloud Computing. Patterns for Concurrent, Parallel, and Distributed Systems. Douglas C. Schmidt, ``Wrapper Facade: A Structural Pattern for Encapsulating Functions within Classes,'' C++ Report, SIGS, Vol. 11, No 2, February, 1999.
This paper describes the Wrapper Facade pattern. The intent of this pattern is to encapsulate low-level, stand-alone functions with object-oriented (OO) class interfaces. Common examples of the Wrapper Facade pattern are C++ wrappers for native OS C APIs, such as sockets or pthreads. 10 Ideas. Published in 1991 Prelude "Show me. " John held up the chalk, holding it by the top, the bottom pointed at his feet. His smile was slight but visible, visible to everyone in the lecture hall; his smile was aimed at me. Who was more scared, the students, or me - the TA? That’s something no one can ever know, but there were 50 of them and one of me; if my answer is wrong, their answers would have been wrong. Three months earlier, in my first quarter as graduate student at Stanford, in the Lab at the top of the hill, just before a volleyball game, I asked John McCarthy - the John McCarthy - whether I could have an office at the Lab and be supported by it. "Sure," if I TAed 206, the Lisp course.
Some call his classes "Uncle John’s Mystery Hour," in which John McCarthy can and will lecture on the last thing he thought of before rushing late through the door and down the stairs to the front of the lecture hall. So now you’re thinking that the problem was stated funny. "Nah. Introduction Samefringe. GHC/Data Parallel Haskell. 1 Data Parallel Haskell Searching for Parallel Haskell? DPH is a fantastic effort, but it's not the only way to do parallelism in Haskell.
Try the Parallel Haskell portal for a more general view. Data Parallel Haskell is the codename for an extension to the Glasgow Haskell Compiler and its libraries to support nested data parallelism with a focus to utilise multicore CPUs. Nested data parallelism extends the programming model of flat data parallelism, as known from parallel Fortran dialects, to irregular parallel computations (such as divide-and-conquer algorithms) and irregular data structures (such as sparse matrices and tree structures).
An introduction to nested data parallelism in Haskell, including some examples, can be found in the paper Nepal – Nested Data-Parallelism in Haskell. This is the performance of a dot product of two vectors of 10 million doubles each using Data Parallel Haskell. 1.1 Project status DPH focuses on irregular data parallelism. 1.2 Where to get it 1.3 Overview.