Algorithm optimization for weighted gene co-expression network analysis: accelerating the calculation of Topology Overlap Matrices with OpenMP and SQLite

MotivationWeighted gene co-expression network analysis (WGCNA) is an R package that can search highly related gene modules. The most time-consuming step of the whole analysis is to calculate the Topological Overlap Matrix (TOM) from the Adjacency Matrix in a single thread. This study changes it to multithreading.ResultsThis paper uses SQLite for multi-threaded data transfer between R and C++, uses OpenMP to enable multi-threading and calculates the TOM via an adjacency matrix on a Shared-memory MultiProcessor (SMP) system, where the calculation time decreases as the number of physical CPU cores increases.Availability and implementationThe source code is available at https://github.com/do-somethings-haha/[email protected]

Cache Coherence Protocols in Distributed Systems

Distributed systems performance is affected significantly by cache coherence protocols due to their role in data consistency maintaining. Also, cache coherent protocols have a great task for keeping the interconnection of caches in a multiprocessor environment. Moreover, the overall performance of distributed shared memory multiprocessor system is influenced by the used cache coherence protocol type. The major challenge of shared memory devices is to maintain the cache coherently. Therefore, in past years many contributions have been presented to address the cache issues and to improve the performance of distributed systems. This paper reviews in a systematic way a number of methods used for the cache-coherent protocols in a distributed system.

Beliefs propagation in log domain: a neural inspired algorithm for machine learning

In this paper, we consider a variant of belief propagation algorithm in a tree graphical model where computations are carried out in the negative log-likelihood domain. Unlike the min-product algorithm, our goal is not limited to estimating the mode of the marginal distribution. We would like to obtain the entire marginal distribution as the sum-product algorithm does. We applied the algorithm to learn effective users features for A/B testing. We discussed scalable extension to the proposed algorithm for processing large amount of data.The primary goal of a parallel program is to reduce running time comparing to the sequential program by taking full advantage of computing power of multiprocessors. Threads are widely used in the implementation of parallelism in shared memory multiprocessor architectures. For UNIX/LINUX systems, pthread is the POSIX standard threading interface, which provides support a standardized way for creating and synchronizing threads. Here we presents how pthreads can be used successfully in parallelizing real scientific problems. We will illustrate it by implementing the shared memory parallel version of Jacobi iteration algorithm. Results of performance tests showed that the speedups can be up to p where p is the number of processors.

Beliefs propagation in log domain: a neural inspired algorithm for machine learning

In this paper, we consider a variant of belief propagation algorithm in a tree graphical model where computations are carried out in the negative log-likelihood domain. Unlike the min-product algorithm, our goal is not limited to estimating the mode of the marginal distribution. We would like to obtain the entire marginal distribution as the sum-product algorithm does. We applied the algorithm to learn effective users features for A/B testing. We discussed scalable extension to the proposed algorithm for processing large amount of data.The primary goal of a parallel program is to reduce running time comparing to the sequential program by taking full advantage of computing power of multiprocessors. Threads are widely used in the implementation of parallelism in shared memory multiprocessor architectures. For UNIX/LINUX systems, pthread is the POSIX standard threading interface, which provides support a standardized way for creating and synchronizing threads. Here we presents how pthreads can be used successfully in parallelizing real scientific problems. We will illustrate it by implementing the shared memory parallel version of Jacobi iteration algorithm. Results of performance tests showed that the speedups can be up to p where p is the number of processors.