1. GPU Computation in Bioinspired
Algorithms. A review
M.G. Arenas, A.M. Mora, G. Romero, P.A. Castillo
Depto. Arquitectura y Tecnología de Computadores

2. GPU Computation in Bioinspired Algorithms. A review
1. Introduction
• Growing interest in GPU computation
• A GPU is able to perform graphics manipulation at high speed
• Developers can write their own high-level programs to run on GPU
• These architectures suited to run large computational problems
(bioinformatics area)

3. GPU Computation in Bioinspired Algorithms. A review
1. Introduction
• GPUs can be seen as SIMD multi-core processors
• Internally the GPU contains a number of small processors that are
used to perform calculations
• Depending on the GPU, the number of threads that can be
executed in parallel is in the order of hundreds

4. GPU Computation in Bioinspired Algorithms. A review
Index
1. Introduction
2. Throughput, parallelism and GPUs
3. GPUs Programming
4. Bioinspired Methods on GPUs
5. Conclusions

5. GPU Computation in Bioinspired Algorithms. A review
2. Throughput, parallelism and GPUs
• For years, processor makers increased clock rates and instruction-
level paralelism => sequential code run faster
• Nowadays, they design multicore-chips
=> software adapted to
multithreaded / multiprocess

6. GPU Computation in Bioinspired Algorithms. A review
2. Throughput, parallelism and GPUs
• GPUs have matured, becoming general
purpose computational devices for highly
parallel work-loads

7. GPU Computation in Bioinspired Algorithms. A review
Index
1. Introduction
2. Throughput, parallelism and GPUs
3. GPUs Programming
4. Bioinspired Methods on GPUs
5. Conclusions

8. GPU Computation in Bioinspired Algorithms. A review
3. GPUs Programming. Programming Model
• APIs based on C-like languages
• nVidia => GeForce => CUDA
• ATI => Radeon => Close to metal
• Aple => OpenCL
• Microsoft => DirectCompute

9. GPU Computation in Bioinspired Algorithms. A review
3. GPUs Programming. Programming Model
• OpenCL applications:
• GPUs, multi-core CPUs, FPGAs
• portable across different platforms
• keep functionality and correctness

10. GPU Computation in Bioinspired Algorithms. A review
3. GPUs Programming. Execution Model
• Applications have serial portions and parallel portions
(kernel)
• A kernel applies a single stream of instructions to many
data (SIMD)

11. GPU Computation in Bioinspired Algorithms. A review
3. GPUs Programming. Execution Model
• Terminology:
– Each piece of data = work-item (thread)
– A kernel has thousands of work-items
– A kernel is organized into many work-groups (thread block)
– Each work-group process many work-items

12. GPU Computation in Bioinspired Algorithms. A review
3. GPUs Programming. Memory Model
• Define how the data is stored and communicated
between CPU and GPU
• Global memory
CPU->rw / work-items->rw
• Constant memory
CPU->rw / work-items->ro
• Private memory
CPU->X / single work-item->rw
• Local memory
CPU->X / work-group->rw

13. GPU Computation in Bioinspired Algorithms. A review
Index
1. Introduction
2. Throughput, parallelism and GPUs
3. GPUs Programming
4. Bioinspired Methods on GPUs
5. Conclusions

14. GPU Computation in Bioinspired Algorithms. A review
4. Bioinspired Methods on GPUs
• Review of different evolutionary computation
approaches using GPU
• Master-slave
• Fine-grained
• Island-model
• ANN on GPUs

15. GPU Computation in Bioinspired Algorithms. A review
4.1 Master-slave approaches:
• Some actions are executed in the CPU (main loop)
• Evaluation and mutation are run on GPU
• Competition and selection are performed on the CPU, while
mutation, reproduction and evaluation on the GPU
CPU
GPU

16. GPU Computation in Bioinspired Algorithms. A review
4.1 Master-slave approaches
References
• Zhang, S., He, Z.: Implementation of parallel genetic algorithm based on cuda.
In et al., Z.C., Lecture Notes in Computer Science, vol 5821. 2009
• Wong, M., Wong, T., Fok, K.: Parallel evolutionary algorithms on graphics
processing unit. CEC 2005
• Harding, S., Banzhaf, W.: Fast genetic programming and artificial
developmental systems on gpus. High Performance Computing Systems and
Applications. 2007

17. GPU Computation in Bioinspired Algorithms. A review
4.2 Fine-grained approaches:
• The whole evolutionary process is run on the GPU
• Each EA individual is set to each GPU processor
• Store individuals and fitness in the GPU global memory
• Problem: random number generation (on CPU)

18. GPU Computation in Bioinspired Algorithms. A review
4.2 Fine-grained approaches
References
• Wong, M., Wong, T.: Parallel hybrid genetic algorithms on Consumer-Level
graphics hardware. CEC 2006
• Wong, M., Wong, T.: Implementation of parallel genetic algorithms on graphics
processing units. Studies in Computational Intelligence, vol 187, pp. 197–216. 2009
• Yu, Q., Chen, C., Pan, Z.: Parallel genetic algorithms on programmable graphics
hardware. Lecture Notes in Computer Science, vol 3612, pp. 1051–1059. 2005
• Luo, Z., Liu, H.: Cellular genetic algorithms and local search for 3-SAT problem on
graphic hardware. CEC 2006
• Li, J.,Wang, X., He, R., Chi, Z.: An efficient fine-grained parallel genetic algorithm
based on GPU-Accelerated. Network and Parallel Computing Workshop. 2007
• Li, J., Zhang, L., Liu, L.: A parallel immune algorithm based on fine-grained model
with gpu-acceleration. International Conference on Innovative Computing. 2009
• Vidal, P., Alba, E.: Cellular genetic algorithm on graphic processing units. NICSO
2010

19. GPU Computation in Bioinspired Algorithms. A review
4.3 Coarse-grained approaches (island
model):
• Code an “island” (EA) on GPU
• Generate the initial population on CPU and copy it to the GPU VRAM
• Each subpopulation evolves in each GPU processor
• At some generations, individuals in subpopulations are shuffled via the
GPU VRAM

20. GPU Computation in Bioinspired Algorithms. A review
4.3 Island-model approaches
References
• Pospichal, P., Jaros., J.: Gpu-based acceleration of the genetic algorithm.
Technical report, GECOO competition (2009)
• Tsutsui, S., Fujimoto, N.: Solving quadratic assignment problems by genetic
algorithms with gpu computation: a case study. GECCO 2009
• Luong, T.V., Melab, N., Talbi, E.G.: GPU-based Island Model for Evolutionary
Algorithms. GECCO 2010
• Pospichal, P., Jaros, J., Schwarz, J.: Parallel genetic algorithm on the cuda
architecture. Lecture Notes in Computer Science, vol 6024. 2010
• Pospichal, P., Schwarz, J., Jaros, J.: Parallel genetic algorithm solving 0/1
knapsack problem running on the gpu. International Conference on Soft
Computing 2010

21. GPU Computation in Bioinspired Algorithms. A review
4.4 ANN approaches
• Widely used in pattern recognition
• Usually the computation for ANN is inherently parallel
• However, many algorithms require some steps that are
difficult to parallelize

22. GPU Computation in Bioinspired Algorithms. A review
4.4 ANN approaches
References
• http://www.irontaco.com/Documents/MeuthNeuralGPGPUSurvey.pdf
• Kyoung-Su Oh, Keechul Jung, GPU implementation of neural networks, Pattern
Recognition, vol. 37, n.6, 2004
• Z.Luo, H. Liu and X.Wu, Artificial Neural Network Computation on Graphic
Process Unit, IJCNN 2005
• M. Martínez-Zarzuela et al. Fuzzy ART Neural Network Parallel Computing on
the GPU. IWANN 2007
Libraries:
• http://www.codeproject.com/KB/graphics/GPUNN.aspx

23. GPU Computation in Bioinspired Algorithms. A review
Index
1. Introduction
2. Throughput, parallelism and GPUs
3. GPUs Programming
4. Bioinspired Methods on GPUs
5. Conclusions

24. GPU Computation in Bioinspired Algorithms. A review
5. Conclusions
• GPU computing approach
• Overview current programming languages and
software tools
• Review the use of GPUs to implement
bioinspired algorithms

25. GPU Computation in Bioinspired Algorithms. A review
5. Conclusions
• Most of the bioinspired methods use the GPU to
speed-up the fitness evaluation
• Competition and selection are performed on CPU
• Fitness, mutation and reproduction are performed
on GPU
• Speed-ups up to several thousands times higher
on GPU compared to CPU sequential versions

26. GPU Computation in Bioinspired Algorithms. A review
Thank you!
pedro@atc.ugr.es
http://atc.ugr.es/~pedro/research/gpu/