Bridging cultures that have often been distant, Julia combines expertise from the diverse fields of computer science and computational science to create a new approach to numerical computing. Julia is designed to be easy and fast and questions notions generally held to be “laws of nature" by practitioners of numerical computing: \beginlist \item High-level dynamic programs have to be slow. \item One must prototype in one language and then rewrite in another language for speed or deployment. \item There are parts of a system appropriate for the programmer, and other parts that are best left untouched as they have been built by the experts. \endlist We introduce the Julia programming language and its design---a dance between specialization and abstraction. Specialization allows for custom treatment. Multiple dispatch, a technique from computer science, picks the right algorithm for the right circumstance. Abstraction, which is what good computation is really about, recognizes what remains the same after dif...

Julia: A Fresh Approach to Numerical Computing

Bridging cultures that have often been distant, Julia combines expertise from the diverse fields of computer science and computational science to create a new approach to numerical computing. Julia is designed to be easy and fast. Julia questions notions generally held as "laws of nature" by practitioners of numerical computing: 
1. High-level dynamic programs have to be slow. 
2. One must prototype in one language and then rewrite in another language for speed or deployment, and 
3. There are parts of a system for the programmer, and other parts best left untouched as they are built by the experts. 
We introduce the Julia programming language and its design --- a dance between specialization and abstraction. Specialization allows for custom treatment. Multiple dispatch, a technique from computer science, picks the right algorithm for the right circumstance. Abstraction, what good computation is really about, recognizes what remains the same after differences are stripped away. Abstractions in mathematics are captured as code through another technique from computer science, generic programming. 
Julia shows that one can have machine performance without sacrificing human convenience.

Determining the best set of optimizations to apply to a kernel to be executed on the graphics processing unit (GPU) is a challenging problem. There are large sets of possible optimization configurations that can be applied, and many applications have multiple kernels. Each kernel may require a specific configuration to achieve the best performance, and moving an application to new hardware often requires a new optimization configuration for each kernel. In this work, we apply optimizations to GPU code using HMPP, a high-level directive-based language and source-to-source compiler that can generate CUDA / OpenCL code. However, programming with high-level languages may mean a loss of performance compared to using low-level languages. Our work shows that it is possible to improve the performance of a high-level language by using auto-tuning. We perform auto-tuning on a large optimization space on GPU kernels, focusing on loop permutation, loop unrolling, tiling, and specifying which loop(s) to parallelize, and show results on convolution kernels, codes in the PolyBench suite, and an implementation of belief propagation for stereo vision. The results show that our auto-tuned HMPP-generated implementations are significantly faster than the default HMPP implementation and can meet or exceed the performance of manually coded CUDA / OpenCL implementations.

/pdf/auto-tuning-a-high-level-language-targeted-to-gpu-codes-2n88koahpp.pdf

Auto-tuning a high-level language targeted to GPU codes

DMTCP (Distributed MultiThreaded CheckPointing) is a transparent user-level checkpointing package for distributed applications. Checkpointing and restart is demonstrated for a wide range of over 20 well known applications, including MATLAB, Python, TightVNC, MPICH2, OpenMPI, and runCMS. RunCMS runs as a 680 MB image in memory that includes 540 dynamic libraries, and is used for the CMS experiment of the Large Hadron Collider at CERN. DMTCP transparently checkpoints general cluster computations consisting of many nodes, processes, and threads; as well as typical desktop applications. On 128 distributed cores (32 nodes), checkpoint and restart times are typically 2 seconds, with negligible run-time overhead. Typical checkpoint times are reduced to 0.2 seconds when using forked checkpointing. Experimental results show that checkpoint time remains nearly constant as the number of nodes increases on a medium-size cluster. DMTCP automatically accounts for fork, exec, ssh, mutexes/ semaphores, TCP/IP sockets, UNIX domain sockets, pipes, ptys (pseudo-terminals), terminal modes, ownership of controlling terminals, signal handlers, open file descriptors, shared open file descriptors, I/O (including the readline library), shared memory (via mmap), parent-child process relationships, pid virtualization, and other operating system artifacts. By emphasizing an unprivileged, user-space approach, compatibility is maintained across Linux kernels from 2.6.9 through the current 2.6.28. Since DMTCP is unprivileged and does not require special kernel modules or kernel patches, DMTCP can be incorporated and distributed as a checkpoint-restart module within some larger package.

/pdf/dmtcp-transparent-checkpointing-for-cluster-computations-and-vqt4poqomz.pdf

DMTCP: Transparent checkpointing for cluster computations and the desktop

MATLAB is one of the most widely used mathematical computing environments in technical computing. It is an interactive environment that provides high-performance computational routines and an easy-to-use, C-like scripting language. It started out as an interactive interface to EISPACK and LINPACK and has remained a serial program. In 1995, C. Moler of Mathworks argued that there was no market at the time for a parallel MATLAB. But times have changed and we are seeing increasing interest in developing a parallel MATLAB, from both academic and commercial sectors. In a recent survey, 27 parallel MATLAB projects have been identified. We expand upon that survey and discuss the approaches the projects have taken to parallelize MATLAB. Also, we describe innovative features in some of the parallel MATLAB projects. Then we will conclude with an idea of a "right" parallel MATLAB. Finally we will give an example of what we think is a "right" parallel MATLAB: MATLAB*P.

/pdf/parallel-matlab-doing-it-right-1r2zwuly42.pdf

Parallel MATLAB: Doing it Right

: Star-P is an interactive parallel scientific computing environment. It aims to make parallel programming more accessible. Star-P borrows ideas from Matlab-P, but is a new development. Currently only a Matlab interface for Star-P is available, but STAR-P is not limited to being a parallel Matlab. It combines all four parallel Matlab approaches in one environment, as described in the parallel Matlab survey: embarrassingly parallel, message passing, backend support and compilation. It also integrates several parallel numerical libraries into one single easy-to-use piece of software. The focus of Star-P is to improve user productivity in parallel programming. We believe that Star-P can dramatically reduce the difficulty of programming parallel computers by reducing the time needed for development and debugging. To achieve productivity, it is important that the user interface is intuitive to the user. For example, a large class of scientific users are already familiar with the Matlab language. So it is beneficial to use Matlab as a parallel programming language. Additions to the language are minimal in keeping with the philosophy to avoid re-learning. Also, as a design goal, our system does not distinguish between serial data and parallel data.

/pdf/star-p-high-productivity-parallel-computing-541krjgdqv.pdf

Star-P: High Productivity Parallel Computing

MATLAB [?] is one of the most widely used mathematical computing environments in technical computing. It is an interactive environment that provides high performance computational routines and an easy-to-use, C-like scripting language. Mathworks, the company that develops MATLAB, currently does not provide a version of MATLAB that can utilize parallel computing [?]. This has led to academic and commercial efforts outside Mathworks to build a parallel MATLAB, using a variety of approaches. In a survey [?], 26 parallel MATLAB projects utilizing four different approaches have been identified. MATLAB*P [?] is one of the 26 projects. It makes use of the backend support approach. This approach provides parallelism to MATLAB programs by relaying MATLAB commands to a parallel backend. The main difference between MATLAB*P and other projects that make use of the same approach is in its focus. MATLAB*P aims to provide a user-friendly supercomputing environment in which parallelism is achieved transparently through the use of objected oriented programming features in MATLAB. One advantage of this approach is that existing scripts can be run in parallel with no or minimal modifications. This paper describes MATLAB*P 2.0, which is a complete rewrite of MATLAB*P. This new version brings together the backend support approach with embarrassingly parallel and MPI approaches to provide the first complete parallel MATLAB framework.

MATLAB*P 2.0: A unified parallel MATLAB

MATLAB [7] is one of the most widely used mathematical computing environments in technical computing. It is an interactive environment that provides high performance computational routines and an easy-to-use, C-like scripting language. Mathworks, the company that develops MATLAB, currently does not provide a version of MATLAB that can utilize parallel computing [9]. This has led to academic and commercial efforts outside Mathworks to build a parallel MATLAB, using a variety of approaches. MATLAB*P is a parallel MATLAB that focus on enhancing productivity by providing an easy to use parallel computing tool. Using syntaxes identical to regular MATLAB, it can be used to solve large scale algebraic problems as well as multiple small problems in parallel. This paper describes how the innovative combination of ’*p mode’ and ’MultiMATLAB/MultiOctave mode’ in MATLAB*P can be used to solve a large range of real world problems.

Solving Multiple Classes of Problems in Parallel with MATLAB*P

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Papers

Parallel MATLAB: Doing it Right

Star-P: High Productivity Parallel Computing

MATLAB*P 2.0: A unified parallel MATLAB

Solving Multiple Classes of Problems in Parallel with MATLAB*P