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One important feature of a parallel programming model is deterministic semantics: that is, a program produces the same output on every execution with a given input, regardless of the parallel schedule chosen. Determinism makes parallel programs much easier to write, understand, debug, and maintain. Further, many (though not all) parallel programs are, in fact, intended to have deterministic semantics. However, general-purpose languages, particularly those that allow arbitrary updates to shared data, typically do not guarantee determinism, leaving the burden on the programmer to ensure that the program is correctly synchronized. In this talk, I will present Deterministic Parallel Java (DPJ), an extension of the Java programming language that guarantees deterministic semantics in the presence of aliasing and updates to shared mutable objects. An important consequence of deterministic semantics is that a working sequential program needs no further testing after parallelization with DPJ. In DPJ, the programmer partitions the heap into regions and annotates method definitions with read and write effects on those regions. The programmer also explicitly marks parallel tasks and synchronization points. The compiler checks that the method effects are correctly summarized, and that the program is correctly synchronized to ensure determinism. I will describe the core language features, illustrate them with examples, and describe the results of an initial evaluation of the language. I will also describe ongoing work, including features we plan to add to the language. ©2009 Microsoft Corporation. All rights reserved.

A common approach in designing parallel languages is to provide some high level handles to manipulate the use of the parallel platform. This exposes some aspects of the target platform, for example, shared vs. distributed memory or task parallelism vs. data parallelism. Depending on the circumstances, these handles might be too powerful, not powerful enough or impose unnecessary constraints. Many potential programmers avoid these languages because they require too much understanding of parallelism. Many applications written this way don't come close to achieving their potential performance. Applications written in one such language often need to be rewritten for a parallel platform that is sufficiently different. Our goal is to significantly widen the set of programmers who can program for parallel targets by providing an approach that doesn't require any understanding of parallelism, while maximizing both the flexibility for optimization and the potential for reuse of the same program. Instead of viewing the language design problem as one of providing the programmer with high level handles, we view the problem as one of designing an interface. On one side of this interface is the programmer (domain expert) who knows the application but needs no knowledge of any aspects of the platform. On the other side of the interface is the performance expert (programmer or program) who demands maximal flexibility for optimizing the mapping to a wide range of target platforms (parallel / serial, shared / distributed, homogeneous / heterogeneous, etc.) but needs no knowledge of the domain. The separation of the description of the algorithm from the description of the mapping has several benefits. It allows the description of the algorithm to be stable across a much wider range of platforms. It allows for development of the algorithm by a domain expert who has no understanding or interest in parallelism. It allows for tuning by a performance expert who has little or no understanding of the domain. It facilitates independent development of the algorithm and the tuning even if they are performed by the same individual. ©2008 Microsoft Corporation. All rights reserved.

In this paper, we present a new algorithm for parallel Monte Carlo tree search (MCTS). It is based on the pipeline pattern and allows flexible management of the control flow of the operations in parallel MCTS. The pipeline pattern provides for the first structured parallel programming approach to MCTS. Moreover, we propose a new lock-free tree data structure for parallel MCTS which removes synchronization overhead. The Pipeline Pattern for Parallel MCTS algorithm (called 3PMCTS), scales very well to higher numbers of cores when compared to the existing methods.

https://annas-archive.org/md5/0415c8cdc503eb8fdb60065f55a10606 Open Library:- OL19896611W

The initial version of PRISM uses a simulation of the ZMOB hardware, and has been fully tested and debugged. In addition, several enhancements were made to PRISM to permit experimental analyses to be made, and to incorporate additional features to take full advantage of parallelism in a problem solving environment. Tracing and a statistical gathering package were added to permit experimental analysis. An AND-parallelism capability was added to achieve a second version of the PRISM system, and other features were added to the system to more fully exploit parallelism. Preliminary application and evaluation studies were performed.

Parallel programming requires task scheduling to optimize performance; this primarily involves balancing the load over the processors. In many cases, it is critical to perform task scheduling at runtime. For example, (1) in many parallel applications the task load cannot be accurately predicted a priori; (2) in a network-based multicomputer the computational power of each processor may not remain constant. In order to support dynamic task scheduling, the programmer, usually needs to design and implement a complex set of scheduling routines, e.g., routines for maintaining task lists and handling interprocessor communication for load balancing. Unfortunately, it is very difficult and time-consuming to write and debug all of these scheduling routines. This thesis proposes a new approach which can greatly reduce the effort of developing efficient dynamic task scheduling routines. In our new approach, we decompose task scheduling into two parts - the specification of scheduling policies and the implementation of supportive scheduling operations - and then hide the latter from the programmer. We call this approach multilist scheduling, because it is based on a uniform scheduling model involving the use of multiple scheduling lists.

Many important applications have a heterogeneous structure, and can be implemented efficiently only with task parallelism. This paper presents a set of extensions to Fortran to build task parallel programs for multicomputers consisting of distinct nodes: either private memory parallel machines, or autonomous computers connected by a high speed network. The design of these extensions is driven by the following objectives: (1) The compiler should handle inter-node communication, not the programmer; (2) The task extensions should be integrated into an existing parallel programming language, so that existing programs can benefit from task parallelism, existing libraries can be used, and the users do not have to learn a new language; and (3) Current compiler technology should be able to produce efficient parallel programs that are competitive with hand parallelized codes.

These are the lecture notes for CS 15-840B, a hands-on class in programming parallel algorithms. The class was taught in the fall of 1992 by Guy Blelloch, using the programming, language NESL. It stressed the clean and concise expression of a variety of parallel algorithms. About 35 graduate students attended the class', of whom 28 took it for credit. These notes were written by students in the class, and were then reviewed and organized by Guy Blelloch and Jonathan Hardwick. The sample NESL code has been converted from the older LISP-style syntax into the new NlL-style syntax. These notes are not in a polished form, and probably contain several errors and omissions, particularly with respect to references in the literature. Corrections are welcome.

45 p. 28 cm

Includes bibliographical references (p. 391-393) and indexes

Includes bibliographical references (p. 391-393) and indexes

Includes bibliographical references (p. 391-393) and indexes

Includes bibliographical references (p. 391-393) and indexes

We present a library of generic software components for parallel and distributed computations on graphs, based on the Boost Graph Library (BGL). The BGL consists of a rich set of generic graph algorithms and supporting data structures, but it was not originally designed with parallelism in mind. In this talk, we revisit the abstractions comprising the BGL in the context of distributed-memory parallelism, lifting away the implicit requirements of sequential execution and a single shared address space. We illustrate our approach by describing the process as applied to one of the core algorithms in the BGL, breadth-first search. The result is a generic algorithm that is unchanged from the sequential algorithm, requiring only the introduction of external (distributed) data structures for parallel execution. More importantly, the generic implementation retains its interface and semantics, such that other distributed algorithms can be built upon it, just as algorithms are layered in the sequential case. By characterizing these extensions as well as the extension process, we develop general principles and patterns for using (and reusing) generic parallel software libraries. We demonstrate that the resulting algorithm implementations are both efficient and scalable with performance results for several algorithms implemented in the open-source Parallel Boost Graph Library. ©2007 Microsoft Corporation. All rights reserved.

We introduce process-oriented programming as a natural extension of object-oriented programming for parallel computing. It is based on the observation that every class of an object-oriented language can be instantiated as a process, accessible via a remote pointer. The introduction of process pointers requires no syntax extension, identifies processes with programming objects, and enables processes to exchange information simply by executing remote methods. Process-oriented programming is a high-level language alternative to multithreading, MPI and many other languages, environments and tools currently used for parallel computations. It implements natural object-based parallelism using only minimal syntax extension of existing languages, such as C++ and Python, and has therefore the potential to lead to widespread adoption of parallel programming. We implemented a prototype system for running processes using C++ with MPI and used it to compute a large three-dimensional Fourier transform on a computer cluster built of commodity hardware components. Three-dimensional Fourier transform is a prototype of a data-intensive application with a complex data-access pattern. The process-oriented code is only a few hundred lines long, and attains very high data throughput by achieving massive parallelism and maximizing hardware utilization.

We introduce process-oriented programming as a natural extension of object-oriented programming for parallel computing. It is based on the observation that every class of an object-oriented language can be instantiated as a process, accessible via a remote pointer. The introduction of process pointers requires no syntax extension, identifies processes with programming objects, and enables processes to exchange information simply by executing remote methods. Process-oriented programming is a high-level language alternative to multithreading, MPI and many other languages, environments and tools currently used for parallel computations. It implements natural object-based parallelism using only minimal syntax extension of existing languages, such as C++ and Python, and has therefore the potential to lead to widespread adoption of parallel programming. We implemented a prototype system for running processes using C++ with MPI and used it to compute a large three-dimensional Fourier transform on a computer cluster built of commodity hardware components. Three-dimensional Fourier transform is a prototype of a data-intensive application with a complex data-access pattern. The process-oriented code is only a few hundred lines long, and attains very high data throughput by achieving massive parallelism and maximizing hardware utilization.

his research investigates the combination of task and data parallel language constructs within a single programming language. There are an number of applications that exhibit properties which would be well served by such an integrated language. Examples include global climate models, aircraft design problems, and multidisciplinary design optimization problems. Our approach incorporates data parallel language constructs into an existing, object oriented, task parallel language. The language will support creation and manipulation of parallel classes and objects of both types (task parallel and data parallel). Ultimately, the language will allow data parallel and task parallel classes to be used either as building blocks or managers of parallel objects of either type, thus allowing the development of single and multi-paradigm parallel applications. 1995 Research Accomplishments In February I presented a paper at Frontiers '95 describing the design of the data parallel language subset. During the spring I wrote and defended my dissertation proposal. Since that time I have developed a runtime model for the language subset. I have begun implementing the model and hand-coding simple examples which demonstrate the language subset. I have identified an astrophysical fluid flow application which will validate the data parallel language subset. 1996 Research Agenda Milestones for the coming year include implementing a significant portion of the data parallel language subset over the Legion system. Using simple hand-coded methods, I plan to demonstrate (1) concurrent task and data parallel objects and (2) task parallel objects managing both task and data parallel objects. My next steps will focus on constructing a compiler and implementing the fluid flow application with the language. Concurrently, I will conduct a search for a real-world application exhibiting both task and data parallelism within the same program m. Additional 1995 Activities During the fall I collaborated with Andrew Grimshaw and Adam Ferrari to write a book chapter which will be included in Parallel Processing in C edited by Gregory Wilson. I also finished two courses, Compilers and Advanced Compilers, in 1995. These courses complete my class requirements at the University of Virginia. I have only my dissertation research and defense to complete.

How do we get programmers to routinely write parallel software? We have been working in earnest on this problem for over 25 years; actually much longer if you consider that the first multi-threaded machine appeared in 1958 (the Gamma 60 by Bull). But at this point, I’m not sure we’re really getting any closer to solving it. If we study the history of parallel programming, it is clear that in order to solve the parallel programming problem we need to: (1) understand how people write parallel software (mine the key design patterns), (2) agree on how to discuss programmability, and (3) stop scaring away our software developers. In this talk, I will describe these issues and how my (our?) collaborations at the UC Berkeley’s ParLab are addressing them. ©2009 Microsoft Corporation. All rights reserved.

One important feature of a parallel programming model is deterministic semantics: that is, a program produces the same output on every execution with a given input, regardless of the parallel schedule chosen. Determinism makes parallel programs much easier to write, understand, debug, and maintain. Further, many (though not all) parallel programs are, in fact, intended to have deterministic semantics. However, general-purpose languages, particularly those that allow arbitrary updates to shared data, typically do not guarantee determinism, leaving the burden on the programmer to ensure that the program is correctly synchronized. In this talk, I will present Deterministic Parallel Java (DPJ), an extension of the Java programming language that guarantees deterministic semantics in the presence of aliasing and updates to shared mutable objects. An important consequence of deterministic semantics is that a working sequential program needs no further testing after parallelization with DPJ. In DPJ, the programmer partitions the heap into regions and annotates method definitions with read and write effects on those regions. The programmer also explicitly marks parallel tasks and synchronization points. The compiler checks that the method effects are correctly summarized, and that the program is correctly synchronized to ensure determinism. I will describe the core language features, illustrate them with examples, and describe the results of an initial evaluation of the language. I will also describe ongoing work, including features we plan to add to the language. ©2009 Microsoft Corporation. All rights reserved.

Experiments were conducted at NASA Ames Research Center to define multi-tasking software requirements for multiple-instruction, multiple-data stream (MIMD) computer architectures. The focus was on specifying solutions for algorithms in the field of computational fluid dynamics (CFD). The program objectives were to allow researchers to produce usable parallel application software as soon as possible after acquiring MIMD computer equipment, to provide researchers with an easy-to-learn and easy-to-use parallel software language which could be implemented on several different MIMD machines, and to enable researchers to list preferred design specifications for future MIMD computer architectures. Analysis of CFD algorithms indicated that extensions of an existing programming language, adaptable to new computer architectures, provided the best solution to meeting program objectives. The CoFORTRAN Language was written in response to these objectives and to provide researchers a means to experiment with parallel software solutions to CFD algorithms on machines with parallel architectures.

Mesham parallel programming language dumped with WikiTeam tools.

"This report is the text of a paper to be presented in May, 1987, at the Second International Conference on Supercomputing."

Includes bibliographical references (p. [295]-299) and indexes

Includes bibliographical references (p. [295]-299) and indexes

strand-new-concepts-in-parallel-programming-by-foster-ian-taylor-stephen_202201

International Journal of Parallel Programming 1992: Volume 21 , Issue CONTENTS. Digitized from IA1631414-07 . Previous issue: sim_international-journal-of-parallel-programming_1991-12_20_6 . Next issue: sim_international-journal-of-parallel-programming_1992-02_21_1 .

International Journal of Parallel Programming 1992: Volume 21 , Issue CONTENTS. Digitized from IA1631414-07 . Previous issue: sim_international-journal-of-parallel-programming_1991-12_20_6 . Next issue: sim_international-journal-of-parallel-programming_1992-02_21_1 .

I present Chorus, a high-level parallel programming model targeting irregular, heap-manipulating applications like mesh refinement and triangulation, and Jchorus, an implementation of the model on top of sequential Java. One focus of Chorus is dynamism: the fact that in irregularly parallel applications, ownership and isolation are usually instance-dependent and highly dynamic. The other is locality: the property that in many of the same applications, typical imperative commands only affect small, local regions in the heap. Chorus addresses dynamism and locality through the unifying abstraction of an *object assembly*, a short-lived, speculative task that owns a local region in a shared data structure. The task can only access objects that it owns; while changes in ownership are frequent, they are local – i.e., objects only move from one assembly to a neighboring one – and linear – i.e., aliasing and copying are tightly controlled. Programming primitives include a merge-operation, by which an assembly merges with an adjacent assembly, and a split-operation, which linearly splits an assembly into smaller ones. Our abstractions are race and deadlock-free, and inherently data-centric. We demonstrate that Chorus and Jchorus allow natural programming of several important applications exhibiting irregular data-parallelism. From preliminary investigations, it demonstrates competitive performance as well. I finish with a discussion of current work on fractional ownership in Chorus, and on the integration of Chorus with the Habanero-Java language from Rice University. ©2009 Microsoft Corporation. All rights reserved.

I present Chorus, a high-level parallel programming model targeting irregular, heap-manipulating applications like mesh refinement and triangulation, and Jchorus, an implementation of the model on top of sequential Java. One focus of Chorus is dynamism: the fact that in irregularly parallel applications, ownership and isolation are usually instance-dependent and highly dynamic. The other is locality: the property that in many of the same applications, typical imperative commands only affect small, local regions in the heap. Chorus addresses dynamism and locality through the unifying abstraction of an *object assembly*, a short-lived, speculative task that owns a local region in a shared data structure. The task can only access objects that it owns; while changes in ownership are frequent, they are local – i.e., objects only move from one assembly to a neighboring one – and linear – i.e., aliasing and copying are tightly controlled. Programming primitives include a merge-operation, by which an assembly merges with an adjacent assembly, and a split-operation, which linearly splits an assembly into smaller ones. Our abstractions are race and deadlock-free, and inherently data-centric. We demonstrate that Chorus and Jchorus allow natural programming of several important applications exhibiting irregular data-parallelism. From preliminary investigations, it demonstrates competitive performance as well. I finish with a discussion of current work on fractional ownership in Chorus, and on the integration of Chorus with the Habanero-Java language from Rice University. ©2009 Microsoft Corporation. All rights reserved.

I present Chorus, a high-level parallel programming model targeting irregular, heap-manipulating applications like mesh refinement and triangulation, and Jchorus, an implementation of the model on top of sequential Java. One focus of Chorus is dynamism: the fact that in irregularly parallel applications, ownership and isolation are usually instance-dependent and highly dynamic. The other is locality: the property that in many of the same applications, typical imperative commands only affect small, local regions in the heap. Chorus addresses dynamism and locality through the unifying abstraction of an *object assembly*, a short-lived, speculative task that owns a local region in a shared data structure. The task can only access objects that it owns; while changes in ownership are frequent, they are local – i.e., objects only move from one assembly to a neighboring one – and linear – i.e., aliasing and copying are tightly controlled. Programming primitives include a merge-operation, by which an assembly merges with an adjacent assembly, and a split-operation, which linearly splits an assembly into smaller ones. Our abstractions are race and deadlock-free, and inherently data-centric. We demonstrate that Chorus and Jchorus allow natural programming of several important applications exhibiting irregular data-parallelism. From preliminary investigations, it demonstrates competitive performance as well. I finish with a discussion of current work on fractional ownership in Chorus, and on the integration of Chorus with the Habanero-Java language from Rice University. ©2009 Microsoft Corporation. All rights reserved.

I present Chorus, a high-level parallel programming model targeting irregular, heap-manipulating applications like mesh refinement and triangulation, and Jchorus, an implementation of the model on top of sequential Java. One focus of Chorus is dynamism: the fact that in irregularly parallel applications, ownership and isolation are usually instance-dependent and highly dynamic. The other is locality: the property that in many of the same applications, typical imperative commands only affect small, local regions in the heap. Chorus addresses dynamism and locality through the unifying abstraction of an *object assembly*, a short-lived, speculative task that owns a local region in a shared data structure. The task can only access objects that it owns; while changes in ownership are frequent, they are local – i.e., objects only move from one assembly to a neighboring one – and linear – i.e., aliasing and copying are tightly controlled. Programming primitives include a merge-operation, by which an assembly merges with an adjacent assembly, and a split-operation, which linearly splits an assembly into smaller ones. Our abstractions are race and deadlock-free, and inherently data-centric. We demonstrate that Chorus and Jchorus allow natural programming of several important applications exhibiting irregular data-parallelism. From preliminary investigations, it demonstrates competitive performance as well. I finish with a discussion of current work on fractional ownership in Chorus, and on the integration of Chorus with the Habanero-Java language from Rice University. ©2009 Microsoft Corporation. All rights reserved.

I present Chorus, a high-level parallel programming model targeting irregular, heap-manipulating applications like mesh refinement and triangulation, and Jchorus, an implementation of the model on top of sequential Java. One focus of Chorus is dynamism: the fact that in irregularly parallel applications, ownership and isolation are usually instance-dependent and highly dynamic. The other is locality: the property that in many of the same applications, typical imperative commands only affect small, local regions in the heap. Chorus addresses dynamism and locality through the unifying abstraction of an *object assembly*, a short-lived, speculative task that owns a local region in a shared data structure. The task can only access objects that it owns; while changes in ownership are frequent, they are local – i.e., objects only move from one assembly to a neighboring one – and linear – i.e., aliasing and copying are tightly controlled. Programming primitives include a merge-operation, by which an assembly merges with an adjacent assembly, and a split-operation, which linearly splits an assembly into smaller ones. Our abstractions are race and deadlock-free, and inherently data-centric. We demonstrate that Chorus and Jchorus allow natural programming of several important applications exhibiting irregular data-parallelism. From preliminary investigations, it demonstrates competitive performance as well. I finish with a discussion of current work on fractional ownership in Chorus, and on the integration of Chorus with the Habanero-Java language from Rice University. ©2009 Microsoft Corporation. All rights reserved.

linda programming

linda programming

linda programming

This paper presents an overview of the "Applied Parallel Computing" course taught to final year Software Engineering undergraduate students in Spring 2014 at NUST, Pakistan. The main objective of the course was to introduce practical parallel programming tools and techniques for shared and distributed memory concurrent systems. A unique aspect of the course was that Java was used as the principle programming language. The course was divided into three sections. The first section covered parallel programming techniques for shared memory systems that include multicore and Symmetric Multi-Processor (SMP) systems. In this section, Java threads was taught as a viable programming API for such systems. The second section was dedicated to parallel programming tools meant for distributed memory systems including clusters and network of computers. We used MPJ Express-a Java MPI library-for conducting programming assignments and lab work for this section. The third and the final section covered advanced topics including the MapReduce programming model using Hadoop and the General Purpose Computing on Graphics Processing Units (GPGPU).

Experimenting with Chapel Programming Language - a parallel processing programming language by Cray the Supercomputer people. It uses as much parallelism as you have available. This is a dual core laptop, so it splits up hello, world across the 2 cores efficiently. If I had 2 million? Same.

We evaluate optimized parallel sparse matrix-vector operations for two representative application areas on widespread multicore-based cluster configurations. First the single-socket baseline performance is analyzed and modeled with respect to basic architectural properties of standard multicore chips. Going beyond the single node, parallel sparse matrix-vector operations often suffer from an unfavorable communication to computation ratio. Starting from the observation that nonblocking MPI is not able to hide communication cost using standard MPI implementations, we demonstrate that explicit overlap of communication and computation can be achieved by using a dedicated communication thread, which may run on a virtual core. We compare our approach to pure MPI and the widely used "vector-like" hybrid programming strategy.

We evaluate optimized parallel sparse matrix-vector operations for two representative application areas on widespread multicore-based cluster configurations. First the single-socket baseline performance is analyzed and modeled with respect to basic architectural properties of standard multicore chips. Going beyond the single node, parallel sparse matrix-vector operations often suffer from an unfavorable communication to computation ratio. Starting from the observation that nonblocking MPI is not able to hide communication cost using standard MPI implementations, we demonstrate that explicit overlap of communication and computation can be achieved by using a dedicated communication thread, which may run on a virtual core. We compare our approach to pure MPI and the widely used "vector-like" hybrid programming strategy.

We evaluate optimized parallel sparse matrix-vector operations for two representative application areas on widespread multicore-based cluster configurations. First the single-socket baseline performance is analyzed and modeled with respect to basic architectural properties of standard multicore chips. Going beyond the single node, parallel sparse matrix-vector operations often suffer from an unfavorable communication to computation ratio. Starting from the observation that nonblocking MPI is not able to hide communication cost using standard MPI implementations, we demonstrate that explicit overlap of communication and computation can be achieved by using a dedicated communication thread, which may run on a virtual core. We compare our approach to pure MPI and the widely used "vector-like" hybrid programming strategy.

We evaluate optimized parallel sparse matrix-vector operations for two representative application areas on widespread multicore-based cluster configurations. First the single-socket baseline performance is analyzed and modeled with respect to basic architectural properties of standard multicore chips. Going beyond the single node, parallel sparse matrix-vector operations often suffer from an unfavorable communication to computation ratio. Starting from the observation that nonblocking MPI is not able to hide communication cost using standard MPI implementations, we demonstrate that explicit overlap of communication and computation can be achieved by using a dedicated communication thread, which may run on a virtual core. We compare our approach to pure MPI and the widely used "vector-like" hybrid programming strategy.

DokuWiki: Programming Massively Parallel Multiprocessors with CUDA Dumped with DokuWiki-Dumper v0.1.44, and uploaded with dokuWikiUploader v0.1.44.

DokuWiki: Programming Massively Parallel Multiprocessors with CUDA Dumped with DokuWiki-Dumper v0.1.44, and uploaded with dokuWikiUploader v0.1.44.

DokuWiki: Programming Massively Parallel Multiprocessors with CUDA Dumped with DokuWiki-Dumper v0.1.44, and uploaded with dokuWikiUploader v0.1.44.

Moshe Goldstein/david dayan - Implementing Parallel Programming Design Patterns using EFL for Python [EuroPython 2016] [19 July 2016 / 2016-07-19] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/implementing-parallel-programming-design-patterns-using-efl-for-python) EFL (Embedded Flexible Language), a deterministic parallel programming tool, may be embedded in any host language. Two versions of the EFL pre-compiler for Python were implemented. One translates EFL blocks into Python's Multiprocessing code, and the other one into DTM/MPI4PY code. EFL implementations of Parallel Programming Design Patterns will be shown, generated parallel code compared, and differences discussed. Visit flexcomp.jct.ac.il for further information. ----- Multi-core CPUs are abundant and utilizing them effectively requires programmers to parallelize CPU-intensive code. To facilitate this, we have developed EFL (Embedded Flexible Language), a deterministic parallel programming tool. The parallel parts of a program are written as EFL-blocks, which are embedded into a sequential host language program. The sequential parts of the program are written in the host language, outside the EFL blocks. EFL may be embedded in any host language by writing an appropriate EFL pre-compiler. At the moment, we implemented two versions of the EFL pre-compiler. Both pre-compilers translate EFL blocks into parallel Python code - one of them generates parallel code based on Python's Multiprocessing module, and the other one generates parallel code based on the DTM/MPI4PY Python module. We will present the principles upon which EFL is built. We will show the implementation of Parallel Programming Design Patterns using EFL's parallel programming constructs (such as parallel assignments, parallel for-loops, etc.). Using our two EFL pre-compilers we will show their translation to Python parallel code according to the Multiprocessing module as well as the DTM/MPI4PY module. The differences between code versions produced by the EFL pre-compilers will be discussed. For further information about the EFL project and our Flexible Computation Research Laboratory, visit http://flexcomp.jct.ac.il

I'm going to talk about the 4 main levels of parallelism in modern Computing: multiple (virtual) machines multiple processes multiple threads multiple green threads, aka asyncio Why you might use each of them, how to go about doing so with python and some of the pitfalls you might fall into along the way. To do so, I'll give short examples in code of achieving each level: leveraging multiple hosts using RQ, and also the possibility of RPC with HTTP multiprocessing and threading using their respective modules from the python standard library asyncio demonstrated with AIOHTTP That sounds great, but there are ""gotchas"" you should know about before you get started, for example: multiple machines can actually be multiple virtual machines on the same host effectively communicating between processes is hard, how can we go about making it easier? the limitations of threading and the GIL runemin/emexecutor - do we ever really need to use multiprocessing or threading directly again use of asyncio when dealing with both networking between hosts and between processes - you end up using two different kinds of concurrency at the same time. That can be confusing, but also awesome. I'll finish of by showcasing a library I built, arq which is a job queueing and RPC library for python which uses asyncio and Redis. Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/

We present a new programming paradigm which can be useful, in particular, for implementing window interfaces and parallel algorithms. This paradigm allows a user to define operators which can contain nested operators. The new paradigm is called operator-oriented. One of the goals of this paradigm is to escape the complexity of objects definitions inherent in many object-oriented languages and to move to transparent algorithms definitions.

With rapidly evolving technology, multicore and manycore processors have emerged as promising architectures to benefit from increasing transistor numbers. The transition towards these parallel architectures makes today an exciting time to investigate challenges in parallel computing. The TILEPro64 is a manycore accelerator, composed of 64 tiles interconnected via multiple 8x8 mesh networks. It contains per-tile caches and supports cache-coherent shared memory by default. In this paper we present a programming technique to take advantages of distributed caching facilities in manycore processors. However, unlike other work in this area, our approach does not use architecture-specific libraries. Instead, we provide the programmer with a novel technique on how to program future Non-Uniform Cache Architecture (NUCA) manycore systems, bearing in mind their caching organisation. We show that our localised programming approach can result in a significant improvement of the parallelisation efficiency (speed-up).

The Application Portable Parallel Library (APPL) is a subroutine-based library of communication primitives that is callable from applications written in FORTRAN or C. APPL provides a consistent programmer interface to a variety of distributed and shared-memory multiprocessor MIMD machines. The objective of APPL is to minimize the effort required to move parallel applications from one machine to another, or to a network of homogeneous machines. APPL encompasses many of the message-passing primitives that are currently available on commercial multiprocessor systems. This paper describes APPL (version 2.3.1) and its usage, reports the status of the APPL project, and indicates possible directions for the future. Several applications using APPL are discussed, as well as performance and overhead results.

Threads are typically not the way to take advantage of multiple CPUs for CPU-bound problems. The Global Interpreter Lock (GIL) allows the use of only one CPU at the time when using threads. However, the GIL is released for IO task The use case is a scientific simulation model that has to run 18,000 different simulations. The input data for these simulations need to be extracted from a common database, re-assembled and translated into several input files per simulation. After each simulation that is run with an external, standalone executable, the output data needs to be gathered and rearranged in a output database. The implementation scaled up to 50 threads. On a eight-core machine more than 90 % usage of all CPUs could be achieved, bringing the total run time down to about two hours from about 15 hours. Depending on the use case, threading can help to speedup a program and even take advantage of multiple CPUs. This talk presents such a use case. The approach can be translated to problems from other domains if the sub-tasks can be turned into IO tasks. Asynchronous programming could have been used here. However using a thread per task and using class that represents a task, is likely conceptually simpler for programmers not used to asynchronous programming. Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/