Large Scale Network-Centric Distributed Systems

2

Compilers, Techniques, and Tools for Supporting Programming Heterogeneous Many/Multicore Systems

Pasquale Cantiello, Beniamino Di Martino, and Francesco Moscato

Contents

2.1 Introduction

2.2 Programming Models and Tools for Many/Multicore

2.2.1 OpenMP

2.2.2 Brook for GPUs

2.2.3 Sh

2.2.4 CUDA

2.2.4.1 Memory Management

2.2.4.2 Kernel Creation and Invocation

2.2.4.3 Synchronization

2.2.5 HMPP

2.2.6 OpenCL

2.2.7 OpenAcc

2.3 Compilers and Support Tools

2.3.1 RapidMind Multicore Development Platform

2.3.2 OpenMPC

2.3.3 Source-to-Source Transformers

2.3.3.1 CHiLL

2.3.3.2 Cetus

2.3.3.3 ROSE Compiler

2.3.3.4 LLVM

2.4 CALuMET: A Tool for Supporting Software Parallelization

2.4.1 Component-Based Source Code Analysis Architecture

2.4.2 Algorithmic Recognizer Add-on

2.4.3 Source Code Transformer for GPUs

2.5 Conclusion

References

2.1 Introduction

In the last few years, the continuous growth of processors' clock speed has stopped and processor improvements follow a different path by multiplying the number of processing units on a chip. Not only systems for scientific commodity applications but also personal computers include multiple multicore CPUs and GPUs. It is hard to write parallel code or to port existing sequential code to new architectures. It is a costly process requiring skilled developers. This chapter presents, after a brief introduction of different programming models for current many/multicore and GPUs systems, a review of the state of the art of compilers and support tools ...