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Preliminaries
1. Evaluation of State-of-the-Art Parallelizing Compilers Generating CUDA Code for Heterogeneous CPU/GPU Computing, J.C. Juega, S. Verdoolaege, A. Cohen, J.I. Gómez, C. Tenllado and F. Catthoor	1
2. Data Size and Data Type Dynamic GPU Code Generation, H.-P. Charles and V. Lomüller	31
3. High Level GPGPU Programming with Parallel Skeletons, M. Bourgoin, E. Chailloux and J.-L. Lamotte	57
4. Programming GPUs from High Level Data Flow Models, M. Barreteau, R. Barrère and E. Lenormand	73
5. Optimization methodology for Parallel Programming of Homogeneous or Hybrid Clusters, S. Vialle and S. Contassot-Vivier	111
6. Program Sequentially, Carefully, and Benefit from Compiler Advances for Parallel Heterogeneous Computing, M. Amini, C. Ancourt, B. Creusillet, F. Irigoin and R. Keryell	149
7. Using MELT to Improve or Explore your GCC-Compiled Source Code, B. Starynkevitch	171
8. OpenCL: A Suitable Solution to Simplify and Unify High Performance Computing Developments, J. Passerat-Palmbach and D.R.C. Hill	187
9. Parallel Preconditioned Conjugate Gradient Algorithm on GPU, F. Andzembe and J. Koko	209
10. Solving Sparse Linear Systems with CG and GMRES Methods on a GPU and GPU Clusters, R. Couturier and L. Ziane Khodja	227
11. Bioinformatics of Non-Coding RNAs and GPUs, A Case Study: Prediction at Large Scale of MicroRNAs in Genomes, F. Tahi, V.D. Tran, S. Tempel and E. Mahé	249
12. Migrating a Big-Data Grade Application to Large GPU Clusters, D. Tello, V. Ducrot, J.-M. Batto, S. Monot, F. Boumezbeur, V. Arslan and T. Saidani	281
13. Testing Random Numbers: When OpenCL is the Right Choice, J.M. Chauvet and E. Mahé	311
End matter / Index

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