Nnmulti-gpu volume rendering using map reduce pdf file

Citeseerx multigpu volume rendering using mapreduce. Pipelined multigpu mapreduce for bigdata processing. Deep shadow maps unify the computation of volumetric and geometric shadows. Does the algorithm break down into two separate phases i. Implementations that use the 2dtexture capabilities of standard pc hardware. Since the volume of the newly arriving data for a given query can have significant fluctuations, the optimization and sharing tech niques are further complicated by. In defense of mapreduce cheriton school of computer science. Recently, the use of the mapreduce framework for distribu ted rdf schema reasoning has shown that it is possible to compute the. The distance map is used to efficiently skip empty regions and reduce. In this paper, we describe a gpuaccelerated ray casting volume.

Accelerated volume rendering with chebyshev distance maps. Gpuaccelerated deep shadow maps for direct volume rendering. We give implementation details of the library, including specific optimizations made for our rendering and compositing design. Efficiently rendering large volume data using texture mapping hardware xin tong1, wenping wang2, waiwan tsang2, zesheng tang1 1 cad laboratory, department of computer science, tsinghua university, beijing, 84, p. Big data analysis, big data management, map reduce, hdfs. Smooth mixedresolution gpu volume rendering johanna beyer1 markus hadwiger1 torsten moller2 laura fritz1 1vrvis research center, vienna, austria. Big data is a collection of large datasets that cannot be processed using traditional computing techniques. Request pdf pipelined multigpu mapreduce for bigdata processing mapreduce is a popular largescale dataparallel processing model. Google including our experiences in using it as the basis. We analyze the theoretical peak performance and bottlenecks for all.

Volume rendering techniques for general purpose graphics hardware. Crucially, the computation of sparse pdf volumes exploits data coherence in 4d. Our goal of using a sparse representation is not reducing the size of the. Multigpu volume rendering using mapreduce proceedings. Mapreduce represents a specific instance of a general class of. Direct volume rendering is performed with an additional deep shadow map lookup for each. Sparse pdf volumes for consistent multiresolution volume. Shared execution of recurring workloads in mapreduce vldb. In this paper we present a multigpu parallel volume rendering implemention built using the mapreduce programming model. Instead of implementing a mapper and reducer class, a.

This data is categories as big data due to its sheer volume, variety, velocity and veracity. Multigpu volume rendering using mapreduce collaboratory for. Our mapreducebased renderer can produce a giga pixel rendering of a 1 billion triangle mesh in just under two minutes. I decided to use this model to create a framework that runs a mapreduce job on the gpu.

We argue that a multigpu mapreduce library is a good fit for parallel volume renderering. Citeseerx document details isaac councill, lee giles, pradeep teregowda. The high computational complexity of volume rendering is a problem for these. Writing a mapreduce job in marimba is as simple as writing a hadoop job.