The paper introduces an optimized multicore CPU implementation of the genetic algorithm and compares its performance with a fine-tuned GPU version. The main goal is to show the true performance relation between modern CPUs and GPUs and eradicate some of myths surrounding GPU performance. It is essential for the evolutionary community to provide the same conditions and designer effort to both implementations when benchmarking CPUs and GPUs. Here we show the performance comparison supported by architecture characteristics narrowing the performance gain of GPUs
http://deepblue.lib.umich.edu/bitstream/2027.42/3571/5/bab2674.0001.001.pdfhttp://deepblue.lib.umich...
The Gram-Schmidt method is a classical method for determining QR decompositions, which is commonly u...
In this paper, we describe our work to investigate how much cyclic graph based Genetic Programming (...
Abstract—A Genetic Algorithm (GA) is a heuristic to find exact or approximate solutions to optimizat...
Multiple independent runs of an evolutionary algorithm in parallel are often used to increase the ef...
Multiple independent runs of an evolutionary algorithm in parallel are often used to increase the ef...
High-performance computing is one of the most demanding technologies in today\u27s computational wor...
Multiple independent runs of an evolutionary algorithm in parallel are often used to increase the ef...
Evolutionary algorithms (EA) are proven effective and robust in searching large varied spaces in a w...
This paper presents implementation details of GPU-based genetic algorithm submitted to GPUs for Gene...
Genetic Algorithms (GAs) is proven to be effective in solving many optimization tasks. GAs is one of...
Graphic processing units (GPUs) emerged recently as an exciting new hardware environment for a truly...
Nowadays a technique of using graphics processing units (GPUs) for general-purpose computing (or GPG...
This paper propose a multithreaded Genetic Programming classi cation evaluation model using NVIDIA...
Millions of short sequences are produced because of the introduction of next-generation sequencing t...
http://deepblue.lib.umich.edu/bitstream/2027.42/3571/5/bab2674.0001.001.pdfhttp://deepblue.lib.umich...
The Gram-Schmidt method is a classical method for determining QR decompositions, which is commonly u...
In this paper, we describe our work to investigate how much cyclic graph based Genetic Programming (...
Abstract—A Genetic Algorithm (GA) is a heuristic to find exact or approximate solutions to optimizat...
Multiple independent runs of an evolutionary algorithm in parallel are often used to increase the ef...
Multiple independent runs of an evolutionary algorithm in parallel are often used to increase the ef...
High-performance computing is one of the most demanding technologies in today\u27s computational wor...
Multiple independent runs of an evolutionary algorithm in parallel are often used to increase the ef...
Evolutionary algorithms (EA) are proven effective and robust in searching large varied spaces in a w...
This paper presents implementation details of GPU-based genetic algorithm submitted to GPUs for Gene...
Genetic Algorithms (GAs) is proven to be effective in solving many optimization tasks. GAs is one of...
Graphic processing units (GPUs) emerged recently as an exciting new hardware environment for a truly...
Nowadays a technique of using graphics processing units (GPUs) for general-purpose computing (or GPG...
This paper propose a multithreaded Genetic Programming classi cation evaluation model using NVIDIA...
Millions of short sequences are produced because of the introduction of next-generation sequencing t...
http://deepblue.lib.umich.edu/bitstream/2027.42/3571/5/bab2674.0001.001.pdfhttp://deepblue.lib.umich...
The Gram-Schmidt method is a classical method for determining QR decompositions, which is commonly u...
In this paper, we describe our work to investigate how much cyclic graph based Genetic Programming (...