There are several different methods to make an efficient strategy for steganalysis of digital images. A very powerful method in this area is rich model consisting of a large number of diverse sub-models in both spatial and transform domain that should be utilized. However, the extraction of a various types of features from an image is so time consuming in some steps, especially for training pha...

We investigate the use of NVIDIA’s Compute Unified Device Architecture (CUDA) to speed up matching in classifier systems. We compare CUDA-based matching and CPU-based matching on (i) real inputs using interval-based conditions and on (ii) binary inputs using ternary conditions. Our results show that on small problems, due to the memory transfer overhead introduced by CUDA, matching is faster wh...

The main goal of this project was to explore the possibility of applying CUDA to the Point Based Color Bleeding global illumination algorithm. This project tackled the creation of surfels, the storage of surfels in an octree, representation of an octree in CUDA, and the transversal of an octree in CUDA. Future work will include the rasterization step of the Point Based Color Bleeding algorithm ...

Using FPGAs as hardware accelerators that communicate with a central CPU is becoming a common practice in the embedded design world but there is no standard methodology and toolset to facilitate this path yet. On the other hand, languages such as CUDA and OpenCL provide standard development environments for Graphical Processing Unit (GPU) programming. FASTCUDA is a platform that provides the ne...

CUDA and OpenCL offer two different interfaces for programming GPUs. OpenCL is an open standard that can be used to program CPUs, GPUs, and other devices from different vendors, while CUDA is specific to NVIDIA GPUs. Although OpenCL promises a portable language for GPU programming, its generality may entail a performance penalty. In this paper, we compare the performance of CUDA and OpenCL usin...

The proliferation of heterogeneous computing systems has led to increased interest in parallel architectures and their associated programming models. One of the most promising models for heterogeneous computing is the accelerator model, and one of the most cost-effective, high-performance accelerators currently available is the general-purpose, graphics processing unit (GPU). Two similar progra...

The Compute Unified Device Architecture (CUDA) programming environment from NVIDIA is a milestone towards making programming many-core GPUs more flexible to programmers. However, there are still many challenges for programmers when using CUDA. One is how to deal with GPU device memory, and data transfer between host memory and GPU device memory explicitly. In this study, source-to-source compil...

for many years, researchers have studied high accuracy methods for recognizing the handwriting and achieved many significant improvements. however, an issue that has rarely been studied is the speed of these methods. considering the computer hardware limitations, it is necessary for these methods to run in high speed. one of the methods to increase the processing speed is to use the computer pa...

Rapid advancements in multi-core processor architectures along with low-cost, low-latency, high-bandwidth interconnects have made clusters of multi-core machines a common computing resource. Unfortunately, writing good parallel programs to efficiently utilize all the resources in such a cluster is still a major challenge. Programmers have to manually deal with low-level details that should idea...

We design a high-level abstraction of CUDA, called hiCUDA, using compiler directives. It simplifies the tasks in porting sequential applications to NVIDIA GPUs. This paper focuses on the design and implementation of a source-to-source compiler that translates a hiCUDA program into an equivalent CUDA program, and shows that the performance of CUDA code generated by this compiler is comparable to...