Deap 2.1 tutorial how to#
In this introductory tutorial, we teach how to perform the sum of two vectors C=A+B on the OpenCL device and how to retrieve the results from the device memory.
Deap 2.1 tutorial full#
The target audience of OpenCL consists of programmers that aim at programming portable heterogeneous code and that want full control of the parallelization process. However, this portability has a cost, that’s the reason why OpenCL exposes the programmer to a much lower level compared to OpenACC or even CUDA. OpenCL came as a standard for heterogeneous programming that enables a code to run in different platforms, such as multicore CPUs, GPUs (AMD, Intel, ARM), FPGAs, Apple M1, tensor cores, and ARM processors with minor or no modifications.įurthermore, differently from OpenACC, the programmer has full control of the hardware and is entirely responsible for the parallelization process. However, OpenACC is a proprietary parallel programming model and it is supported by a limited set of devices, such as NVIDIA GPUs.
In this programming model, the parallelism is implicit, which means that the compiler is responsible for the parallelization, which might not work in all scenarios. In the HPC school, the students had the opportunity to work with a higher-level heterogeneous programming model based on directives, called OpenACC. Introduction to OpenCL Programming (C/C++) Copyright (c) T.
Accelerating Applications with CUDA C/C++.It is also possible to use one of the four algorithms readilyĪvailable in the algorithms module, or build from some buildingīlocks called variations also available in this module.
values = fit # The population is entirely replaced by the offspring pop = offspring return pop
Deap 2.1 tutorial zip#
evaluate, invalid_ind ) for ind, fit in zip ( invalid_ind, fitnesses ): ind. values # Evaluate the individuals with an invalid fitness invalid_ind = fitnesses = map ( toolbox. values for mutant in offspring : if random. clone, offspring ) # Apply crossover and mutation on the offspring for child1, child2 in zip ( offspring, offspring ): if random. select ( pop, len ( pop )) # Clone the selected individuals offspring = map ( toolbox. values = fit for g in range ( NGEN ): # Select the next generation individuals offspring = toolbox. evaluate, pop ) for ind, fit in zip ( pop, fitnesses ): ind. population ( n = 50 ) CXPB, MUTPB, NGEN = 0.5, 0.2, 40 # Evaluate the entire population fitnesses = map ( toolbox.
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