Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
OOPSLA '04 Proceedings of the 19th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
BEE2: A High-End Reconfigurable Computing System
IEEE Design & Test
Reconfigurable computing for learning Bayesian networks
Proceedings of the 16th international ACM/SIGDA symposium on Field programmable gate arrays
Merge: a programming model for heterogeneous multi-core systems
Proceedings of the 13th international conference on Architectural support for programming languages and operating systems
Roofline: an insightful visual performance model for multicore architectures
Communications of the ACM - A Direct Path to Dependable Software
Towards program optimization through automated analysis of numerical precision
Proceedings of the 8th annual IEEE/ACM international symposium on Code generation and optimization
ParaLearn: a massively parallel, scalable system for learning interaction networks on FPGAs
Proceedings of the 24th ACM International Conference on Supercomputing
Embracing heterogeneity: parallel programming for changing hardware
HotPar'09 Proceedings of the First USENIX conference on Hot topics in parallelism
ParaLearn: a massively parallel, scalable system for learning interaction networks on FPGAs
Proceedings of the 24th ACM International Conference on Supercomputing
Bridging the GPGPU-FPGA efficiency gap
Proceedings of the 19th ACM/SIGDA international symposium on Field programmable gate arrays
Optimizing parallel belief propagation in junction treesusing regression
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
FPGA implementation of particle swarm optimization for Bayesian network learning
Computers and Electrical Engineering
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Aberrant intracellular signaling plays an important role in many diseases. The causal structure of signal transduction networks can be modeled as Bayesian Networks (BNs), and computationally learned from experimental data. However, learning the structure of Bayesian Networks (BNs) is an NP-hard problem that, even with fast heuristics, is too time consuming for large, clinically important networks (20--50 nodes). In this paper, we present a novel graphics processing unit (GPU)-accelerated implementation of a Monte Carlo Markov Chain-based algorithm for learning BNs that is up to 7.5-fold faster than current general-purpose processor (GPP)-based implementations. The GPU-based implementation is just one of several implementations within the larger application, each optimized for a different input or machine configuration. We describe the methodology we use to build an extensible application, assembled from these variants, that can target a broad range of heterogeneous systems, e.g., GPUs, multicore GPPs. Specifically we show how we use the Merge programming model to efficiently integrate, test and intelligently select among the different potential implementations.