Computation on programmable graphics hardware

Goodnight, Nolan; Wang, R.; Humphreys, Greg

doi:10.1109/mcg.2005.101

Cited by 26 publications

(8 citation statements)

References 3 publications

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“…The main reason for this is a GPU allows to efficiently run the Render operator. Alternatively also many other approaches exist mapping originally CPU-based algorithms to a GPU-based version [10], [11]. We can also think about CPU-based algorithms mapped to FPGA-based algorithms to enhance the accuracy or efficiency, such as a complex function for interpolation.…”

Section: Classification Of Constraint Typesmentioning

confidence: 99%

Exploiting constraints to build a flexible and extensible data stream processing middleware

Cipriani

Lübbe

Moosbrugger

2010

2010 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum (IPDPSW)

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A wide range of real-time applications process stream-based data. To process this stream-based data in an application-independent manner, many stream processing systems have been built. However, none of them reached a huge domain of applications, such as databases did. This is due to the fact that they do not consider the specific needs of real-time applications. For instance, an application which visualizes streambased data has stringent timing constraints, or may even need a specific hardware environment to smoothly process the data. Furthermore, users may even add additional constraints. E.g., for security reasons they may want to restrict the set of nodes that participates in processing. Thus, constraints naturally arise on different levels of query processing.In this work we classify constraints that occur on different levels of query processing. Furthermore we propose a scheme to classify the constraints and show how these can be integrated into the query processing of the distributed data stream middleware NexusDS.

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Section: Classification Of Constraint Typesmentioning

confidence: 99%

Exploiting constraints to build a flexible and extensible data stream processing middleware

Cipriani

Lübbe

Moosbrugger

2010

2010 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum (IPDPSW)

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“…The light and transformation operations on vertices that were present in the vertex stage of the earlier design of the graphics pipeline have been replaced by a user-defined vertex program; moreover, the fixed fragment operations that took place in the fragment stage have been replaced with a user-defined fragment program. "A programmer can now implement custom transformation, lighting, or texturing algorithms by writing programs called shaders" (8).…”

Section: Gpgpumentioning

confidence: 99%

GPUs: An Emerging Platform for General-Purpose Computation

Boggan¹,

Pressel²

2007

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Approved for public release; distribution is unlimited.ii REPORT DOCUMENTATION PAGE Form Approved OMB No. 0704-0188Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY) August 20072. REPORT TYPE ARL-SR-154 SPONSOR/MONITOR'S ACRONYM(S) 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release; distribution is unlimited. SUPPLEMENTARY NOTES ABSTRACTThe affordability and computational power of GPUs have made them the focus of an emerging area of research designed to explore their performance for general-purpose computation. GPGPU, as this area of research is called, involves the exploration of the computational power of programmable GPUs and their suitability for non-graphics applications through algorithm and software development. Although there are some challenges with using these specialized devices for numerous applications, their attributes and significant speedup for some applications continue to make them an attractive platform for research. iii SUBJECT TERMS

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“…GPU is just used to accelerate some stages of the graphics or video rendering traditionally for many years. During the last years, modern GPU can now perform more functions than the specific graphics computations for which they have fully programmable floating-point pipelines and tremendous aggregate computational power and memory bandwidth [5]. This paper describes a new implemented method using GPU processing power to accelerate the MPEG audio layer III (MP3) decoding.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of MPEG audio layer III decoder using graphics processing units

Chen

Chengshi

et al. 2010

2010 International Conference on Image Analysis and Signal Processing

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This paper describes a new implemented method for the MPEG audio layer III (MP3) decoder. The proposed architecture is based on a graphic process unit (GPU) using CUDA environment, where it can effectively take advantage of modern GPU's parallel computing power. The implemented system with this architecture employs a multithread model and memory optimization to process MP3 decoding in parallel, so it is significant to minimize the computational overhead. Experimental results on a GTX260+ graphics card showed that the proposed architecture is over five times faster than traditional MP3 library based on CPU.

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Computation on programmable graphics hardware

Cited by 26 publications

References 3 publications

Exploiting constraints to build a flexible and extensible data stream processing middleware

Exploiting constraints to build a flexible and extensible data stream processing middleware

GPUs: An Emerging Platform for General-Purpose Computation

Design and implementation of MPEG audio layer III decoder using graphics processing units

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