This paper proposes an approach to derive a parametric L-system in parallel based on Compute Unified Device Architecture (CUDA). It consists of a host program running on CPU and a device program running on CUDA enabled GPU. The host program is used to transfer data between CPU and GPU, pre-allocate host and device memory, and launch the device program. The device program takes charge of derivin...

Recent work in deep learning has created a voracious demand for more compute cycles, but with the slowdown of Moore’s law, CPUs cannot keep up with the demand. Thus, attention has turned to massively-parallel hardware accelerators, ranging from new processing units designed specifically for deep learning to the repurposing of existing technologies like Fieldprogrammable gate arrays (FPGAs) and ...

In computer graphics, ray tracing is a popular technique for rendering images with computers. In this project we implemented a serial version of ray tracing in C, and three parallelized versions in CUDA. We conducted experiments to demonstrate speedup with CUDA, as well as the importance of balancing workload among threads.

In this paper, I use the NVIDIA CUDA technology to perform the chroma key algorithm on stereoscopic images. NVIDIA CUDA allows to process parallel algorithms on GPU. Input data are stereoscopic images with the monochromatic background and the destination background image. Output data is the combination of inputs by using the chroma key. I compare the algorithm efficiency between the GPU and CPU...

Push-based database management system (DBMS) is a new type of data processing software that streams large volume of data to concurrent query operators. The high data rate of such systems requires large computing power provided by the query engine. In our previous work, we built a push-based DBMS named G-SDMS to harness the unrivaled computational capabilities of modern GPUs. A major design goal...

GPUs have been successfully applied in scientific computing in the last decade. Many machine learning algorithms have also used GPUs to accelerate their computations. This includes the Support Vector Machine (SVM) which is a classical machine learning algorithm that has been successfully used in many applications such as text classification and image recognition. There have been many open-sourc...

A recent trend in mainstream desktop systems is the use of general-purpose graphics processor units (GPGPUs) to obtain order-ofmagnitude performance improvements. CUDA has emerged as a popular programming model for GPGPUs for use by C/C++ programmers. Given the widespread use of modern object-oriented languages with managed runtimes like Java and C#, it is natural to explore how CUDA-like capab...

Sparse Matrix is a matrix consisting of very few non-zero entries. Large sparse matrices are often used in engineering and scientific operations. Especially sparse-matrix vector multiplication is an important operation for solving linear system and partial differential equations. However, there is a possibility that even though the matrix is partitioned and stored appropriately, the performance...

NVIDIA’s Compute Unified Device Architecture (CUDA) and its attached C/C++ based API went a long way towards making GPUs more accessible to mainstream programming. So far, the use of GPUs for high performance computing has been primarily restricted to data parallel applications, and with good reason. The high number of computational cores and high memory bandwidth supported by the device makes ...