Abstract
AbstractNetwork analysis typically involves as set of repetitive tasks that are particularly amenable to poor-man's parallelization. This is therefore an ideal application are for GPU architectures, which help to alleviate the tedium inherent to statistically sound analysis of network data. Here we will illustrate the use of GPUs in a range of applications, which include percolation processes on networks, the evolution of protein-protein interaction networks, and the fusion of different types of biomedical and disease data in the context of molecular interaction networks. We will pay particular attention to the numerical performance of different routines that are frequently invoked in network analysis problems. We conclude with a review over recent developments in the generation of random numbers that address the specific requirements posed by GPUs and high-performance computing needs.
Highlights
Run Time on single threads C/C++ have better performance characteristics
Prototyping/Development Time is typically reduced for scripting languages such as R or Python
Energy Requirements Every Watt we use for computing we have to extract with air conditioning
Summary
Prototyping/Development Time is typically reduced for scripting languages such as R or Python. Run Time on single threads C/C++ (or Fortran) have better performance characteristics. For specialized tasks other languages, e.g. Haskell, can show good characteristics. Energy Requirements Every Watt we use for computing we have to extract with air conditioning. The role of GPUs GPUs can be accessed from many different programming languages (e.g. PyCUDA). GPUs have a comparatively small footprint and relatively modest energy requirements compared to clusters of CPUs. GPUs were designed for consumer electronics: computer gamers have different needs from the HPC community
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