Gene co-expression networks comprise one type of valuable biological networks. Many methods and tools have been published to construct gene co-expression networks; however, most of these tools and methods are inconvenient and time consuming for large datasets. We have developed a user-friendly, accelerated and optimized tool for constructing gene co-expression networks that can fully harness th...

In this study computations of the two-dimensional Direct Simulation Monte Carlo (DSMC) method using Graphics Processing Units (GPUs) are presented. An all-device (GPU) computational approach is adopted – where the entire computation is performed on the GPU device, leaving the CPU idle which includes particle moving, indexing, collisions between particles and state sampling. The subsequent appli...

In this paper, we look at the acceleration of weakly coupled electromechanics using the graphics processing unit (GPU). Specifically, we port to the GPU a number of components of CHeart--a CPU-based finite element code developed for simulating multi-physics problems. On the basis of a criterion of computational cost, we implemented on the GPU the ODE and PDE solution steps for the electrophysio...

Biological computations like electrocardiological modelling and simulation usually require high-performance computing environments. This paper introduces an implementation of parallel computation for computer simulation of electrocardiograms (ECGs) in a personal computer environment with an Intel CPU of Core (TM) 2 Quad Q6600 and a GPU of Geforce 8800GT, with software support by OpenMP and CUDA...

In this contribution, a single-node multi-GPU thermal lattice Boltzmann solver is presented. The program is based on the TheLMA framework which was developed for the purpose. The chosen implementation and optimisation strategies are described, both for inter-GPU communication and for coupling with the thermal component of the model. Validation and performance results are provided as well.

Most compilers have a single core intermediate representation (IR) (e.g., LLVM) sometimes complemented with vaguely defined IR-like data structures. This IR is commonly low-level and close to machine instructions. As result, optimizations relying on domain-specific information are either not possible or require complex analysis recover the missing information. In contrast, multi-level rewriting...

There is an increased interest in building data analytics frameworks with advanced algebraic capabilities both in industry and academia. Many of these frameworks, e.g., TensorFlow and BIDMach, implement their computeintensive primitives in two flavors—as multi-thread routines for multi-core CPUs and as highly-parallel kernels executed on GPU. Stochastic gradient descent (SGD) is the most popula...