Speeding up Multiple Instance Learning Classification Rules on GPUs

Multiple instance learning is a challenging task in supervised learning and data mining. How- ever, algorithm performance becomes slow when learning from large-scale and high-dimensional data sets. Graphics processing units (GPUs) are being used for reducing computing time of algorithms. This paper presents an implementation of the G3P-MI algorithm on GPUs for solving multiple instance problems using classification rules. The GPU model proposed is distributable to multiple GPUs, seeking for its scal- ability across large-scale and high-dimensional data sets. The proposal is compared to the multi-threaded CPU algorithm with SSE parallelism over a series of data sets. Experimental results report that the com- putation time can be significantly reduced and its scalability improved. Specifically, an speedup of up to 149× can be achieved over the multi-threaded CPU algorithm when using four GPUs, and the rules interpreter achieves great efficiency and runs over 108 billion Genetic Programming operations per second

Working with OpenCL to Speed Up a Genetic Programming Financial Forecasting Algorithm: Initial Results

The genetic programming tool EDDIE has been shown to be a successful financial forecasting tool, however it has suffered from an increase in execution time as new features have been added. Speed is an important aspect in financial problems, especially in the field of algorithmic trading, where a delay in taking a decision could cost millions. To offset this performance loss, EDDIE has been modified to take advantage of multi-core CPUs and dedicated GPUs. This has been achieved by modifying the candidate solution evaluation to use an OpenCL kernel, allowing the parallel evaluation of solutions. Our computational results have shown improvements in the running time of EDDIE when the evaluation was delegated to the OpenCL kernel running on a multi-core CPU, with speed ups up to 21 times faster than the original EDDIE algorithm. While most previous works in the literature reported significantly improvements in performance when running an OpenCL kernel on a GPU device, we did not observe this in our results. Further investigation revealed that memory copying overheads and branching code in the kernel are potentially causes of the (under-)performance of the OpenCL kernel when running on the GPU device

Genetic Programming Approach for Classification Problem using GPU

Genetic programming (GP) is a machine learning technique that is based on the evolution of computer programs using a genetic algorithm. Genetic programming have proven to be a good technique for solving data set classification problems but at high computational cost. The objectives of this research is to accelerate the execution of the classification algorithms by proposing a general model of execution in GPU of the adjustment function of the individuals of the population. The computation times of each of the phases of the evolutionary process and the operation of the model of parallel programming in GPU were studied. Genetic programming is interesting to parallelize from the perspective of evolving a population of individuals in paralle

High performance genetic programming on GPU

The availability of low cost powerful parallel graphics cards has stimulated the port of Genetic Programming (GP) on Graphics Processing Units (GPUs). Our work focuses on the possibilities offered by Nvidia G80 GPUs when pro-grammed in the CUDA language. We compare two par-allelization schemes that evaluate several GP programs in parallel. We show that the fine grain distribution of compu-tations over the elementary processors greatly impacts per-formances. We also present memory and representation op-timizations that further enhance computation speed, up to 2.8 billion GP operations per second. The code has been developed with the well known ECJ library

Genetic Programming Approach for Classification Problem using GPU

Genetic programming (GP) is a machine learning technique that is based on the evolution of computer programs using a genetic algorithm. Genetic programming have proven to be a good technique for solving data set classification problems but at high computational cost. The objectives of this research is to accelerate the execution of the classification algorithms by proposing a general model of execution in GPU of the adjustment function of the individuals of the population. The computation times of each of the phases of the evolutionary process and the operation of the model of parallel programming in GPU were studied. Genetic programming is interesting to parallelize from the perspective of evolving a population of individuals in parallel

Parallel evaluation of Pittsburgh rule-based classifiers on GPUs

Individuals from Pittsburgh rule-based classifiers represent a complete solution to the classification problem and each individual is a variable-length set of rules. Therefore, these systems usually demand a high level of computational resources and run-time, which increases as the complexity and the size of the data sets. It is known that this computational cost is mainly due to the recurring evaluation process of the rules and the individuals as rule sets. In this paper we propose a parallel evaluation model of rules and rule sets on GPUs based on the NVIDIA CUDA programming model which significantly allows reducing the run-time and speeding up the algorithm. The results obtained from the experimental study support the great efficiency and high performance of the GPU model, which is scalable to multiple GPU devices. The GPU model achieves a rule interpreter performance of up to 64 billion operations per second and the evaluation of the individuals is speeded up of up to 3.461× when compared to the CPU model. This provides a significant advantage of the GPU model, especially addressing large and complex problems within reasonable time, where the CPU run-time is not acceptabl

Mixing multi-core CPUs and GPUs for scientific simulation software

Recent technological and economic developments have led to widespread availability of multi-core CPUs and specialist accelerator processors such as graphical processing units (GPUs). The accelerated computational performance possible from these devices can be very high for some applications paradigms. Software languages and systems such as NVIDIA's CUDA and Khronos consortium's open compute language (OpenCL) support a number of individual parallel application programming paradigms. To scale up the performance of some complex systems simulations, a hybrid of multi-core CPUs for coarse-grained parallelism and very many core GPUs for data parallelism is necessary. We describe our use of hybrid applica- tions using threading approaches and multi-core CPUs to control independent GPU devices. We present speed-up data and discuss multi-threading software issues for the applications level programmer and o er some suggested areas for language development and integration between coarse-grained and ne-grained multi-thread systems. We discuss results from three common simulation algorithmic areas including: partial di erential equations; graph cluster metric calculations and random number generation. We report on programming experiences and selected performance for these algorithms on: single and multiple GPUs; multi-core CPUs; a CellBE; and using OpenCL. We discuss programmer usability issues and the outlook and trends in multi-core programming for scienti c applications developers