Using the histogram procedure, this work studies performance determining factors in computing in parallel on SIMD and SIMT devices. Modern graphics pro-cessing units (GPUs) support SIMT, multiple threads running the same instruction, whereas central processing units (CPUs) use SIMD, in which one instruction op-erates on multiple operands. As part of this work, a cross-technology framework is developed that allows testing a single-source histogram implementation on multiple devices, providing insight into the performance of various API – hardwareconfigurations. It is shown that in the presence of high contention, the implementation of atomic operations becomes of great influence on performance. This work provides guidelines for the choice be...
This dissertation presents a hierarchical single-instruction multiple-data (H-SLMD) configurable com...
In a world heading towards applications, in science and industry, based on big data processing, the ...
filtering. • These kernels have a large amount of data-level parallelism. • All these applications a...
Graphics Processing Units (GPUs) are suitable for highly data parallel algorithms such as image proc...
Graphics Processing Units (GPUs) are suitable for highly data parallel algorithms such as image proc...
Abstract—We present two efficient histogram algorithms de-signed for NVIDIA’s compute unified device...
Histograms are used in various fields to quickly profile the distribution of a large amount of data....
Histogramming is a technique by which input datasets are mined to extract features and patterns. His...
Histogramming is a tool commonly used in data analysis. Although its serial version is simple to imp...
The contemporary large scale measuring systems in the real-time environment make extensive use of hi...
The aim of this paper is to present a comparative analysis of the execution times of low-level visio...
This work analyzes the role of graphic processing units (GPUs) in the framework of traditional paral...
Abstract—Histogramming is a tool commonly used in data analysis. Although its serial version is simp...
[[abstract]]The real-time parallel computation of histograms using an array of pipelined cells is pr...
The aim of this document is to explain the procedures followed to develop a concurrent histogramming...
This dissertation presents a hierarchical single-instruction multiple-data (H-SLMD) configurable com...
In a world heading towards applications, in science and industry, based on big data processing, the ...
filtering. • These kernels have a large amount of data-level parallelism. • All these applications a...
Graphics Processing Units (GPUs) are suitable for highly data parallel algorithms such as image proc...
Graphics Processing Units (GPUs) are suitable for highly data parallel algorithms such as image proc...
Abstract—We present two efficient histogram algorithms de-signed for NVIDIA’s compute unified device...
Histograms are used in various fields to quickly profile the distribution of a large amount of data....
Histogramming is a technique by which input datasets are mined to extract features and patterns. His...
Histogramming is a tool commonly used in data analysis. Although its serial version is simple to imp...
The contemporary large scale measuring systems in the real-time environment make extensive use of hi...
The aim of this paper is to present a comparative analysis of the execution times of low-level visio...
This work analyzes the role of graphic processing units (GPUs) in the framework of traditional paral...
Abstract—Histogramming is a tool commonly used in data analysis. Although its serial version is simp...
[[abstract]]The real-time parallel computation of histograms using an array of pipelined cells is pr...
The aim of this document is to explain the procedures followed to develop a concurrent histogramming...
This dissertation presents a hierarchical single-instruction multiple-data (H-SLMD) configurable com...
In a world heading towards applications, in science and industry, based on big data processing, the ...
filtering. • These kernels have a large amount of data-level parallelism. • All these applications a...