nVIDIA CUDA™

Top PDF nVIDIA CUDA™:

Digital Image Processing Algorithms Utilizing NVIDIA CUDA Technology

Digital Image Processing Algorithms Utilizing NVIDIA CUDA Technology

V dnešní době už existuje spousta aplikací využívajících k výpočtům grafické karty a můžeme očekávat, že tento trend bude díky skvělým výpočetním časům pokračovat. Pokud budou podmínky pro vytváření aplikací využívajících grafické karty stále lepší, je možné že takové aplikace budou v budoucnosti naprosto běžné. Možná to bude díky fyzikálním omezením výroby čipů nevyhnutelné. Navíc programování paralelních apli- kací už není tolik složité a věřím, že se bude dále zjednodušovat. Už teď se dá vyvíjet CUDA program pod více známými programovacími jazyky jako jsou C, C++, C#, Java, Fortran, Python a další. Osobně bych hodnotil programování v NVIDIA CUDA jako pro- gramátorsky velmi přívětivé. Od programátora se v základu vyžaduje pochopení běhu CUDA programu, znalost uspořádání vláken ve vytvořené mřížce a porozumění komu- nikace s různými pamětmi grafické karty. Některé algoritmy ale mohou být náročnější na programátorovu představivost. V souhrnu je programování CUDA programu jednodu- ché, dokud není potřeba výrazné zefektivnění kódu. Pak si CUDA vyžaduje znát hlubší znalosti a více souvislostí.
Show more

42 Read more

Acceleration of Image Processing Algorithms with NVIDIA CUDA

Acceleration of Image Processing Algorithms with NVIDIA CUDA

V dnešnej dobe je na trhu k dispozícii veľa programov, ktoré disponujú množstvom nástrojov na úpravu obrazu. Od zmeny rozlíšenia cez automatickú korektúru farieb, až po veľmi zložité filtre, ktoré dokážu dať aj nekvalitným obrázkom nádych profesionality. Tieto úpravy sú mnohokrát veľmi výpočtovo náročné a dokážu skutočne vyťažiť aj moderné 4 a viac jadrové procesory. V mojej práci som sa snažil spracovať bežné metódy využívané na úpravu obrazu do prostredia NVIDIA CUDA a využiť tak výkon grafického akcelerátoru, ktorý je pri práci na počítači mnohokrát úplne nevyužitý. Nové architektúry ako Fermi a v dobe písania práce predstavený Kepler poskytujú masívny paralelný výkon, ktorým by sa dali operácie vykonávané procesorom mnohonásobne zrýchliť.
Show more

47 Read more

GPU Programming using NVIDIA CUDA

GPU Programming using NVIDIA CUDA

Abstract: GPGPU or General-Purpose Computing on Graphics Programming Unit, also called as Heterogeneous Computing, is rapidly emerging into an area of great interest to computer scientists and engineers. A single or a small group of connected GPUs can solve certain classes of computational problems faster than a multicore CPU can. GPUs can effectively and efficiently solve problems that have a high level of parallelization in them. To achieve this sort of GPU programmability, a C-based framework called CUDA exists. CUDA (Compute Unified Device Architecture) is a framework created and maintained by NVIDIA to help simplify the task of GPU programming. This paper presents the foundations of this computational model that can be harnessed for solving complex computational problems, while also mentioning its limitations.
Show more

6 Read more

Optimizing apples lossless audio codec algorithm using NVIDIA CUDA

Optimizing apples lossless audio codec algorithm using NVIDIA CUDA

We understood the importance of compression in modern age because it is easy to keep or delete big size of data but storage remain kind of same for certain time therefore, compression enhance that possibility to store more data. Furthermore, execution time plays a vital role in performance of a algorithm and implementing ALAC in CUDA will help to reduce the execution time which will be useful for converting big size file into .caf format. Furthermore, as it is codec so it is playable for after converting the file. Considering the fact, we implemented ALAC in CUDA using CUDA C from NVIDIA GPU
Show more

40 Read more

1 Efficient Histogram Algorithms for NVIDIA CUDA Compatible Devices

1 Efficient Histogram Algorithms for NVIDIA CUDA Compatible Devices

In our opinion, CUDA is a promising technology built upon a solid hardware and software platform that can greatly benefit scientific and applied computing applications. The two major limitations of the current hardware are the small size of the shared memory and the lack of basic synchronization methods. While we recognize that addressing these limitations is no trivial task, we expect that future generations of the platform to provide further improvements and more flexibility over time.

5 Read more

Optimizing Apple Lossless Audio Codec Algorithm using NVIDIA CUDA Architecture

Optimizing Apple Lossless Audio Codec Algorithm using NVIDIA CUDA Architecture

As majority of the compression algorithms are implementations for CPU architecture, the primary focus of our work was to exploit the opportunities of GPU parallelism in audio compression. This paper presents an implementation of Apple Lossless Audio Codec (ALAC) algorithm by using NVIDIA GPUs Compute Unified Device Architecture (CUDA) Framework. The core idea was to identify the areas where data parallelism could be applied and parallel programming model CUDA could be used to execute the identified parallel components on Single Instruction Multiple Thread (SIMT) model of CUDA. The dataset was retrieved from European Broadcasting Union, Sound Quality Assessment Material (SQAM). Faster execution of the algorithm led to execution time reduction when applied to audio coding for large audios. This paper also presents the reduction of power usage due to running the parallel components on GPU. Experimental results reveal that we achieve about 80-90% speedup through CUDA on the identified components over its CPU implementation while saving CPU power consumption. Keyword:
Show more

6 Read more

GPU acceleration of object classification algorithms using NVIDIA CUDA

GPU acceleration of object classification algorithms using NVIDIA CUDA

The k-Nearest Neighbors extensions provided a speedup of 1.24x and 2.35x over a previous CUDA implementation. These extensions are only active for certain cases, but they have many applications. While these are not huge speedups, they are enhancements over an existing CUDA implementation which reported impressive speedups over sequential implementations. The multi-GPU, multi-class support vector machine exhibited speedups ranging from 89x to 263x over an identical implementation using LIBSVM. The Viola & Jones CUDA implementation exhibited speedups ranging of 1x to 6.5x over OpenCV for image sizes of 300 x 300 pixels up to 2900 x 1600 pixels while having comparable detection results. Additionally, the multi-GPU framework could be customized to process multiple classifiers in parallel.
Show more

94 Read more

Identifying scalar behavior in CUDA kernels

Identifying scalar behavior in CUDA kernels

We propose a compiler analysis pass for programs expressed in the Single Program, Multiple Data (SPMD) programming model. It identifies statically several kinds of regular patterns that can occur between adjacent threads, including common computations, memory accesses at consecutive locations or at the same location and uniform control flow. This knowledge can be exploited by SPMD compilers targeting SIMD architectures. We present a compiler pass developed within the Ocelot framework that performs this analysis on NVIDIA CUDA programs at the PTX intermediate language level. Results are compared with optima obtained by simulation of several sets of CUDA benchmarks.
Show more

19 Read more

What is CUDA? Why do I care about CUDA? What is CUDA not? Getting started Nvidia-Drivers Overclocking...

What is CUDA? Why do I care about CUDA? What is CUDA not? Getting started Nvidia-Drivers Overclocking...

CUDA (an acronym for Compute Unified Device Architecture) is a parallel computing architecture developed by NVIDIA. CUDA lets programmers utilize a dedicated driver written using C language subroutines to offload data processing to the graphics processing hardware found on Nvidia's latemodel GeForce graphics hardware. The software lets programmers use the cards to process data other than just graphics, without having to learn OpenGL or how to talk with the card specifically. Since CUDA tools first emerged in late 2006, Nvidia's seen them used in everything from consumer software to industrial products, and the applications are limitless.
Show more

31 Read more

HCudaBLAST: an implementation of BLAST on Hadoop and Cuda

HCudaBLAST: an implementation of BLAST on Hadoop and Cuda

The evaluation of the HCudaBLAST algorithm is done on a cluster of machines, setup with Hadoop framework and each such machine has Nvidia CUDA capability in it. Two machines in this cluster have Intel Xeon E5-2630 processor and 6 cores with 8 GB RAM and these machines have two CUDA devices, first one is Tesla C2075 with 14 multipro- cessors and computing capability of 2.0 and second one is Quadro 2000 with four mul- tiprocessor and computing capability of 2.1. Another three machines have Intel Xeon E31245 processor and 4 cores with 8 GB RARM and these machines have one CUDA device Quadro 600 with computing capability of 2.1. All the physical machines in a clus- ter was connected with a single switch in a local LAN. The cluster created here running Hadoop version 2.7 and CUDA toolkit 7.5 on each machine. Here, one machine act as a master node which runs NameNode and ResourceManager on it and all other four machines run DataNode and NodeManager on it. All the code run on Hadoop is written in Java programming language and the native code running on GPUs written in C, which called by Java using Native Interface (JNI).
Show more

8 Read more

CUDA: High Parallel Computing Performance

CUDA: High Parallel Computing Performance

GPU computing, or the utilization of graphics processors for general-purpose computing, began in earnest many years ago. Work so far as enclosed a lot of promising analysis in the medical specialty domain. However, this analysis at first concerned programming the GPU via a graphics language, that restricted its flexibility and was arcane for non- graphics consultants. NVIDIA’s CUDA platform changed that, providing a massively multithreaded general purpose architecture with up to 128 processor cores and thousands of many billions of floating point operations each second. CUDA runs on all current NVIDIA GPUs including the HPC-oriented Tesla product. The ubiquitous nature of those GPUs make them a compelling platform for fast high performance computing (HPC) applications.
Show more

8 Read more

CUDA-Accelerated ORB-SLAM for UAVs

CUDA-Accelerated ORB-SLAM for UAVs

The high-level steps of the algorithm remain similar after parallelizing the extraction of the ORB features with CUDA. First, the scale pyramid is precomputed, but using OpenCV’s CUDA GpuMat methods in this implementation. Then, for each pyramid level, an asynchronous CUDA kernel is launched for FAST on the tiles of the image to compute the keypoints. After that, OpenCV’s CUDA implementation of Gaussian Blur is used before computing descriptors for the keypoints by using the same asynchronous kernel tactic as before. In general, all OpenCV functions were replaced with OpenCV CUDA functions where possible. The outline of the this parallelized implementation is summarized below:
Show more

36 Read more

Optimizing Raytracing Algorithm Using CUDA

Optimizing Raytracing Algorithm Using CUDA

In this experiment, only gt 635m has longer processing time than i7-4790. Considering the time of manufacturing, comparing processors speed (675 MHz to 3.6 GHz), and given that the graphic card has very lower level than CPU, the comparison is absurd, and we can ignore it. Nevertheless, as Figure 11 and Figure 12 show, this graphic card overperform other processors. Thus, we can perform parallel process better, since increased power of graphic cards, using Cuda programming, and optimal and appropriate selection of thread number.

12 Read more

CUDA accelerated cone‐beam reconstruction

CUDA accelerated cone‐beam reconstruction

One  way  to  judge  if  a  speedup  is  sufficient  for  a  CUDA  card  is  to  compare  it  to  the  number  of  CUDA  cores  of  that  card.    This  is  because  the  number  of  CUDA  cores  represents  the  number  of  operations  that  can  run  simultaneously  in  parallel.    The  overall  analysis  is  that  the  G210  reached  sufficient  throughput,  the  GTX  295  needs  improvement, and the GTX 560 also reached sufficient throughput.  This is because the  GTX 295 had reached only 50x improvement for a 128 card.  The reasons for this include  the unoptimized sections of the CUDA accelerations.  However, the GTX 560 was able to  run  at  near  peak  efficiency  due  to  its  cache  being  able  to  blunt  the  penalty  of  unoptimized code. 
Show more

131 Read more

NVIDIA PureVideo Decoder User s Guide

NVIDIA PureVideo Decoder User s Guide

On a standard TV or monitor screen, the full image is displayed, but since the original image is wider than your TV or monitor screen, it does not fill the full height of your screen. To fill the screen, NVIDIA PureVideo Decoder generates black ʺmaskingʺ bars above and below the movie image.

36 Read more

Scaling CUDA for Distributed Heterogeneous Processors

Scaling CUDA for Distributed Heterogeneous Processors

utilization improves. For loading global memory, Phalanx uses a minimum fetch size. By default, each load transaction always fetches at least 4-kB of consecutive data. This over-fetching is beneficial most of the time. The coalesced memory access pattern, which is recommended for CUDA programming [13], guarantees that CUDA kernel loads batches of consecutive data. Moreover, the spatial locality of algorithms increases the chance of consumption of nearby data for each load. Since the cache stores all fetched data, any subsequent load transaction can be reduced into a fast cache load if it refers to cached data.
Show more

73 Read more

NVIDIA HPC Update. Carl Ponder, PhD; NVIDIA, Austin, TX, USA - Sr. Applications Engineer, Developer Technology Group

NVIDIA HPC Update. Carl Ponder, PhD; NVIDIA, Austin, TX, USA - Sr. Applications Engineer, Developer Technology Group

COSMO Development on GPUs by MeteoSwiss Climate Simulation and Numerical Weather Prediction Using GPUs. -by Dr[r]

37 Read more

Numerical Ocean Modeling and Simulation with CUDA

Numerical Ocean Modeling and Simulation with CUDA

Because the design of ROMS was not intended for CUDA, we found that porting a function into CUDA and integrating it into the code proved to be difficult tasks. The step2D function is over 2,000 lines long, which meant the equivalent CUDA kernel would be roughly the same length. Although the majority of the original code is directly reused in the kernel, any changes to the code to make the CUDA implementation work (such as the renaming of variables) must be propagated throughout the function. In addition, the step2D function has over 50 parameters, and the total size of these parameters are greater than the 256 KB limit allowed for CUDA kernel arguments. To work around this limitation, we take advantage of a feature in the CUDA Fortran compiler that allows the GPU to access device variables outside the kernel but within the same Fortran module. We change many of the parameters in step2D to module variables. We also encountered challenges relating to encapsulation. Many module variables in ROMS are accessible from any file that imports the module. Because step2D has a length of over 2,000 lines, the function uses module variables from a large number of files. Since GPU memory is separate from CPU memory, all variables used by the function (and therefore the CUDA kernel) must be identified and copied to GPU memory. These variables are often spread across various modules of the program. This lack of encapsulation may need to be considered in future applications of CUDA in ROMS.
Show more

6 Read more

NVIDIA Quadro Professional Drivers Release 175 Notes

NVIDIA Quadro Professional Drivers Release 175 Notes

Antialiasing in the NVIDIA Direct3D driver requires each new frame to be rendered from scratch. This requirement adversely affects applications that render only that portion of the content that has changed since the last frame. A common symptom of this problem is geometric structures that incorrectly disappear and re-appear as the scene shifts.

49 Read more

NVIDIA Mellanox Rivermax Frequently Asked Questions (FAQ)

NVIDIA Mellanox Rivermax Frequently Asked Questions (FAQ)

No license, either expressed or implied, is granted under any NVIDIA patent right, copyright, or other NVIDIA intellectual property right under this document. Information published by NVIDIA regarding third-party products or services does not constitute a license from NVIDIA to use such products or services or a warranty or endorsement thereof. Use of such information may require a license from a third party under the patents or other intellectual property rights of the third party, or a license from NVIDIA under the patents or other intellectual property rights of NVIDIA.
Show more

13 Read more

Show all 175 documents...