## Posts

Jun, 17

### Non-Hydrostatic Pressure Shallow Flows: GPU Implementation Using Finite-Volume and Finite-Difference Scheme

We consider the depth-integrated non-hydrostatic system derived by Yamazaki et al. An efficient formally second-order well-balanced hybrid finite volume/difference numerical scheme is proposed. The scheme consists in a two-step algorithm. First, the hyperbolic part of the system is discretized using a PVM path-conservative finite-volume method. Second, the dispersive terms are solved by means of compact […]

Jun, 17

### Parallel Monte Carlo on Intel MIC Architecture

Trade-off between the cost-efficiency of powerful computational accelerators and the increasing energy needed to perform numerical tasks can be tackled by implementation of algorithms on the Intel Multiple Integrated Cores (MIC) architecture. The best performance of the algorithms requires the use of appropriate optimization and parallelization approaches throughout all process of their design. Monte Carlo […]

Jun, 17

### Parallel Computing of Particle Trajectory Sonification to Enable Real-Time Interactivity

In this paper, we revisit, explore and extend the Particle Trajectory Sonification (PTS) model, which supports cluster analysis of high-dimensional data by probing a model space with virtual particles which are "gravitationally" attracted to a mode of the dataset’s potential function. The particles’ kinetic energy progression of as function of time adds directly to a […]

Jun, 10

### Smith-Waterman Acceleration in Multi-GPUs: A Performance per Watt Analysis

We present a performance per watt analysis of CUDAlign 4.0, a parallel strategy to obtain the optimal alignment of huge DNA se- quences in multi-GPU platforms using the exact Smith-Waterman method. Speed-up factors and energy consumption are monitored on different stages of the algorithm with the goal of identifying advantageous sce- narios to maximize acceleration […]

Jun, 10

### Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous SGD offers a potential solution to this problem by dividing SGD minibatches over a pool of parallel workers. Yet to make this scheme efficient, the per-worker […]

Jun, 10

### Crane – Fast and Migratable GPU Passthrough for OpenCL applications

General purpose GPU (GPGPU) computing in virtualized environments leverages PCI passthrough to achieve GPU performance comparable to bare-metal execution. However, GPU passthrough prevents service administrators from performing virtual machine migration between physical hosts. Crane is a new technique for virtualizing OpenCL-based GPGPU computing that achieves within 5.25% of passthrough GPU performance while supporting VM migration. […]

Jun, 10

### MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU

In this paper, we explore optimizations to run Recurrent Neural Network (RNN) models locally on mobile devices. RNN models are widely used for Natural Language Processing, Machine Translation, and other tasks. However, existing mobile applications that use RNN models do so on the cloud. To address privacy and efficiency concerns, we show how RNN models […]

Jun, 10

### CELES: CUDA-accelerated simulation of electromagnetic scattering by large ensembles of spheres

CELES is a freely available MATLAB toolbox to simulate light scattering by many spherical particles. Aiming at high computational performance, CELES leverages block-diagonal preconditioning, a lookup-table approach to evaluate costly functions and massively parallel execution on NVIDIA graphics processing units using the CUDA computing platform. The combination of these techniques allows to efficiently address large […]

Jun, 5

### Neneta: Heterogeneous Computing Complex-Valued Neural Network Framework

Due to increased demand for computational efficiency for the training, validation and testing of artificial neural networks, many open source software frameworks have emerged. Almost exclusively GPU programming model of choice in such software frameworks is CUDA. Symptomatic is also lack of the support for complex-valued neural networks. With our research going exactly in that […]

Jun, 5

### Speedup and Parallelization Models for Energy-Efficient Many-Core Systems Using Performance Counters

Traditional speedup models, such as Amdahl’s, facilitate the study of the impact of running parallel workloads on manycore systems. However, these models are typically based on software characteristics, assuming ideal hardware behaviors. As such, the applicability of these models for energy and/or performance-driven system optimization is limited by two factors. Firstly, speedup cannot be measured […]

Jun, 5

### Program Acceleration in a Heterogeneous Computing Environment Using OpenCL, FPGA, and CPU

Reaching the so-called "performance wall" in 2004 inspired innovative approaches to performance improvement. Parallel programming, distributive computing, and System on a Chip (SOC) design drove change. Hardware acceleration in mainstream computing systems brought significant improvement in the performance of applications targeted directly to a specific hardware platform. Targeting a single hardware platform, however, typically requires […]

Jun, 5

### UT-OCL: An OpenCL Framework for Embedded Systems Using Xilinx FPGAs

The number of heterogeneous components on a System-on-Chip (SoC) has continued to increase. Software developers leverage these heterogeneous systems by using high-level languages to enable the execution of applications. For the application to execute correctly, hardware support for features and constructs of the programming model need to be incorporated into the system. OpenCL is a […]