Classification of the difficulty in accelerating problems using GPUs
- Authors: Tristram, Uvedale Roy
- Date: 2014
- Subjects: Graphics processing units , Computer algorithms , Computer programming , Problem solving -- Data processing
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:4699 , http://hdl.handle.net/10962/d1012978
- Description: Scientists continually require additional processing power, as this enables them to compute larger problem sizes, use more complex models and algorithms, and solve problems previously thought computationally impractical. General-purpose computation on graphics processing units (GPGPU) can help in this regard, as there is great potential in using graphics processors to accelerate many scientific models and algorithms. However, some problems are considerably harder to accelerate than others, and it may be challenging for those new to GPGPU to ascertain the difficulty of accelerating a particular problem or seek appropriate optimisation guidance. Through what was learned in the acceleration of a hydrological uncertainty ensemble model, large numbers of k-difference string comparisons, and a radix sort, problem attributes have been identified that can assist in the evaluation of the difficulty in accelerating a problem using GPUs. The identified attributes are inherent parallelism, branch divergence, problem size, required computational parallelism, memory access pattern regularity, data transfer overhead, and thread cooperation. Using these attributes as difficulty indicators, an initial problem difficulty classification framework has been created that aids in GPU acceleration difficulty evaluation. This framework further facilitates directed guidance on suggested optimisations and required knowledge based on problem classification, which has been demonstrated for the aforementioned accelerated problems. It is anticipated that this framework, or a derivative thereof, will prove to be a useful resource for new or novice GPGPU developers in the evaluation of potential problems for GPU acceleration.
- Full Text:
- Authors: Tristram, Uvedale Roy
- Date: 2014
- Subjects: Graphics processing units , Computer algorithms , Computer programming , Problem solving -- Data processing
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:4699 , http://hdl.handle.net/10962/d1012978
- Description: Scientists continually require additional processing power, as this enables them to compute larger problem sizes, use more complex models and algorithms, and solve problems previously thought computationally impractical. General-purpose computation on graphics processing units (GPGPU) can help in this regard, as there is great potential in using graphics processors to accelerate many scientific models and algorithms. However, some problems are considerably harder to accelerate than others, and it may be challenging for those new to GPGPU to ascertain the difficulty of accelerating a particular problem or seek appropriate optimisation guidance. Through what was learned in the acceleration of a hydrological uncertainty ensemble model, large numbers of k-difference string comparisons, and a radix sort, problem attributes have been identified that can assist in the evaluation of the difficulty in accelerating a problem using GPUs. The identified attributes are inherent parallelism, branch divergence, problem size, required computational parallelism, memory access pattern regularity, data transfer overhead, and thread cooperation. Using these attributes as difficulty indicators, an initial problem difficulty classification framework has been created that aids in GPU acceleration difficulty evaluation. This framework further facilitates directed guidance on suggested optimisations and required knowledge based on problem classification, which has been demonstrated for the aforementioned accelerated problems. It is anticipated that this framework, or a derivative thereof, will prove to be a useful resource for new or novice GPGPU developers in the evaluation of potential problems for GPU acceleration.
- Full Text:
Buffering strategies and bandwidth renegotiation for MPEG video streams
- Authors: Schonken, Nico
- Date: 1999
- Subjects: Video compression , Computer algorithms , Digital video
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:4651 , http://hdl.handle.net/10962/d1006620 , Video compression , Computer algorithms , Digital video
- Description: This paper confirms the existence of short-term and long-term variation of the required bandwidth for MPEG videostreams. We show how the use of a small amount of buffering and GOP grouping can significantly reduce the effect of the short-term variation. By introducing a number of bandwidth renegotiation techniques, which can be applied to MPEG video streams in general, we are able to reduce the effect of long-term variation. These techniques include those that need the a priori knowledge of frame sizes as well as one that can renegotiate dynamically. A costing algorithm has also been introduced in order to compare various proposals against each other.
- Full Text:
- Authors: Schonken, Nico
- Date: 1999
- Subjects: Video compression , Computer algorithms , Digital video
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:4651 , http://hdl.handle.net/10962/d1006620 , Video compression , Computer algorithms , Digital video
- Description: This paper confirms the existence of short-term and long-term variation of the required bandwidth for MPEG videostreams. We show how the use of a small amount of buffering and GOP grouping can significantly reduce the effect of the short-term variation. By introducing a number of bandwidth renegotiation techniques, which can be applied to MPEG video streams in general, we are able to reduce the effect of long-term variation. These techniques include those that need the a priori knowledge of frame sizes as well as one that can renegotiate dynamically. A costing algorithm has also been introduced in order to compare various proposals against each other.
- Full Text:
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