On Declarative Data-Parallel Computation: Models, Languages and Semantics If we put under analysis the plethora of large-scale data-processing tools avail- able nowadays, we can recognize two main approaches: Although there has been some work trying to bring together the two worlds, these works focus mainly on exporting languages and interfaces — i. We advocate that, instead, a declarative imperative approach should be attempted:
This way software developers can come up with their custom solutions in medical imaging, video analytics, machine vision, video surveillance and similar applications.
Capra uses both task level parallelism for pipeline architecture and data level parallelism for image processing algorithms and has many installations worldwide owing to its optimized real time performance. We even had several implementations on Cuda architecture from NVidia but we were shy to adapt it into our framework as it was only for NVidia and other vendors were working on different architectures and frameworks.
Had we decided to adapt Cuda then we would be spending all our time for adapting other vendors systems within time. In the beginning we decided to wait for Larrabee from Intel in order to see its effects in the market but this wait may never end within years of latency on Larrabee project.
During these waiting periods: With the long time that passed after their acquisition by Intel, finally the team came up with ArBB which has a revolutionary idea being Virtual Machine.
This abstraction is very important and makes ArBB an important player in the field. Whereas it still lacks answers for where does this framework stand. Should it be only supporting IA Intel Architecture and related GPU structures than it would be hard for us to depend on it, they claim opposite for hardware independence but I prefer to wait and see those examples.
Besides the usage in company infrastructure another problem that faced me was my PhD thesis. In order to make something efficient than known methods you should either recommend new algorithms or implement the solutions on new architectures like many core hardware CPU, GPU etc.
Following study is a result of my research for deciding on a structure for a framework which will use parallel programming models inside. Neuroimaging field produces one of the most extensive dataset structures in medical imaging field.
It is very hard to store this dense data for further queries to compare patients functional abilities. Even in compression and data reduction scenarios spatial indexing is necessary for efficient retrieval and query.
Spatial indexing has focus on location and neighboring relations of the data besides its abundance. Since neuroimaging field never involved in clinical applications, speed in data storage and retrieval was never an issue. According to the algorithms availability for parallelism one can reach hundreds of multiplier factors on GPU compared to sequential algorithms on CPU.
The most important parallelism levels today are: Even though GPGPU fastens many data level parallel architectures this advantage is lost in applications where high data transfer or messaging levels are reached. Since it is very hard to transfer data between these structures one should avoid using complex data structures and messaging in their parallel algorithms.
Owing to these structure differences we can deduce that an algorithm implemented for parallel structures may still work with better performance on a multi-core CPU when it could not be efficiently parallelized on GPU.
Studies on spatial indexing structures to make them work on parallel structures sometimes have led to similar results thus a hybrid structure is favored against just the GPGPU model. This was very similar to DirectX monopoly as a part of Microsoft strategy.
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Babbage, The Analytical Engine. Many of the diagrams in the thesis were hand drawn. Client-Server. The first constraints added to our hybrid style are those of the client-server architectural style (), described in Section Separation of concerns is the principle behind the client-server constraints.
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