Writing a modular gpgpu program in java

Terminology[ edit ] The terms multi-core and dual-core most commonly refer to some sort of central processing unit CPUbut are sometimes also applied to digital signal processors DSP and system on a chip SoC. The terms are generally used only to refer to multi-core microprocessors that are manufactured on the same integrated circuit die ; separate microprocessor dies in the same package are generally referred to by another name, such as multi-chip module.

Writing a modular gpgpu program in java

As great scientists have said and as all children know, it is above all by the imagination that we achieve perception, and compassion, and hope. I was part of the Yale Haskell Group. I can be reached at hai dot liu at aya dot yale dot edu.

PC member of Haskell Symposium When higher performance is needed, they are obliged to rewrite their code in a lower-level efficiency language.

writing a modular gpgpu program in java

Different solutions have been proposed to address this trade-off between productivity and efficiency. One promising approach is to create embedded domain-specific languages that sacrifice generality for productivity and performance, but practical experience with DSLs points to some road blocks preventing widespread adoption.

This paper proposes a non-invasive domain-specific language that makes as few visible changes to the host programming model as possible. We present ParallelAccelerator, a library and compiler for high-level, high-performance scientific computing in Julia. Our compiler exposes the implicit parallelism in high-level array-style programs and compiles them to fast, parallel native code.

Programs can also run in "library-only" mode, letting users benefit from the full Julia environment and libraries. Our results show encouraging performance improvements with very few changes to source code required. In particular, few to no additional type annotations are necessary. Jeremy Yallop, Hai Liu.

Causal Commutative Arrows Revisited. Earlier work has revealed that a syntactic transformation of CCA computations into normal form can result in significant performance improvements, sometimes increasing the speed of programs by orders of magnitude.

In this work we reformulate the normalization as a type class instance and derive optimized observation functions via a specialization to stream transformers to demonstrate that the same dramatic improvements can be achieved without leaving the language.

The computationally expensive nature of these networks has led to the proliferation of implementations that sacrifice abstraction for high performance.

In this paper, we present Latte, a domain-specific language for DNNs that provides a natural abstraction for specifying new layers without sacrificing performance.

Users of Latte express DNNs as ensembles of neurons with connections between them. The Latte compiler synthesizes a program based on the user specification, applies a suite of domain-specific and general optimizations, and emits efficient machine code for heterogeneous architectures.

Latte also includes a communication runtime for distributed memory data-parallelism. Hai Liu, Laurence E. Day, Neal Glew, Todd A. Anderson, and Rajkishore Barik. For the most part, existing approaches to programming GPUs within a high-level programming language choose to embed a domain-specific language DSL within a host metalanguage and then implement a compiler that maps programs written within that DSL to code in low-level languages such as OpenCL or CUDA.

We believe more research should be done to compare these two approaches and their relative merits. As a step in this direction, we implemented a quick proof of concept of the alternative approach.

Specifically, we extend the Repa library with a computeG function to offload a computation to the GPU. As long as the requested computation meets certain restrictions, we compile it to OpenCL 2.

We can successfully run nine benchmarks on an Intel integrated GPU. We obtain the expected performance from the GPU on six of those benchmarks, and are close to the expected performance on two more.

In this paper, we describe an offload primitive for Haskell, how to extend Repa to use it, how to implement that primitive in the Intel Labs Haskell Research Compiler, and evaluate the approach on nine benchmarks, comparing to two different CPUs, and for one benchmark to hand-written OpenCL code.

Leaf Petersen, Todd A. Anderson, Hai Liu, and Neal Glew. Measuring The Haskell Gap. Radboud University Nijmegen, The Netherlands. Winner of Peter Landin prize [ abstract bibtex pdf ] Papers on functional language implementations frequently set the goal of achieving performance "comparable to C", and sometimes report results comparing benchmark results to concrete C implementations of the same problem.

A key pair of questions for such comparisons is: In a paper, Satish et al compare naive serial C implementations of a range of throughput-oriented benchmarks to best-optimized implementations parallelized on a six-core machine and demonstrate an average 23x up to 53x speedup.

Even accounting for thread parallel speedup, these results demonstrate a substantial performance gap between naive and tuned C code.Intel's innovation in cloud computing, data center, Internet of Things, and PC solutions is powering the smart and connected digital world we live in.

Publications. Todd A. Anderson, Hai Liu, Lindsey Kuper, Ehsan Totoni, Jan Vitek, Tatiana Shpeisman. Parallelizing Julia with a Non-invasive leslutinsduphoenix.comdings of the European Conference on Object-Oriented Programming (ECOOP'17).

The Lifecycle of a Revolution. In the early days of the public internet, we believed that we were helping build something totally new, a world that would leave behind the shackles of age, of race, of gender, of class, even of law.

Intel's innovation in cloud computing, data center, Internet of Things, and PC solutions is powering the smart and connected digital world we live in.

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writing a modular gpgpu program in java

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