Three-dimensional structure of ribonuclease T-1 complexed with an isosteric phosphonate substrate analogue of GpU: Alternate substrate binding modes and catalysis

The X-ray crystal structure of a complex between ribonuclease T-1 and guanylyl(3'-6')-6'-deoxyhomouridine (GpcU) has been determined at 2.0 Angstrom resolution. This Ligand is an isosteric analogue of the minimal RNA substrate, guanylyl(3'-5')uridine (GpU), where a methylene is substituted for the uridine 5'-oxygen atom. Two protein molecules are part of the asymmetric unit and both have a GpcU bound at the active site in the same manner. The protein-protein interface reveals an extended aromatic stack involving both guanines and three enzyme phenolic groups. A third GpcU has its guanine moiety stacked on His92 at the active site on enzyme molecule A and interacts with GpcU on molecule B in a neighboring unit via hydrogen bonding between uridine ribose 2'- and 3'-OH groups. None of the uridine moieties of the three GpcU molecules in the asymmetric unit interacts directly with the protein. GpcU-active-site interactions involve extensive hydrogen bonding of the guanine moiety at the primary recognition site and of the guanosine 2'-hydroxyl group with His40 and Glu58. on the other hand, the phosphonate group is weakly bound only by a single hydrogen bond with Tyr38, unlike ligand phosphate groups of other substrate analogues and 3'-GMP, which hydrogen-bonded with three additional active-site residues. Hydrogen bonding of the guanylyl 2'-OH group and the phosphonate moiety is essentially the same as that recently observed for a novel structure of a RNase T-1-3'-GMP complex obtained immediately after in situ hydrolysis of exo-(S-p)-guanosine 2',3'-cyclophosphorothioate [Zegers et al. (1998) Nature Struct. Biol. 5, 280-283]. It is likely that GpcU at the active site represents a nonproductive binding mode for GpU [:Steyaert, J., and Engleborghs (1995) fur. J. Biochem. 233, 140-144]. The results suggest that the active site of ribonuclease T-1 is adapted for optimal tight binding of both the guanylyl 2'-OH and phosphate groups (of GpU) only in the transition state for catalytic transesterification, which is stabilized by adjacent binding of the leaving nucleoside (U) group.

Using GPU to exploit parallelism on cryptography

In this article we explore the NVIDIA graphical processing units (GPU) computational power in cryptography using CUDA (Compute Unified Device Architecture) technology. CUDA makes the general purpose computing easy using the parallel processing presents in GPUs. To do this, the NVIDIA GPUs architectures and CUDA are presented, besides cryptography concepts. Furthermore, we do the comparison between the versions executed in CPU with the parallel version of the cryptography algorithms Advanced Encryption Standard (AES) and Message-digest Algorithm 5 (MD5) wrote in CUDA. © 2011 AISTI.

Implementação do algoritmo de treinamento do classificador Floresta de Caminhos Ótimos em GPU

Pós-graduação em Ciência da Computação - IBILCE

Implementação do algoritmo de treinamento do classificador floresta de caminhos ótimos em GPU

Técnicas de reconhecimento de padrões tem como principal objetivo classificar um conjunto de amostras, sendo o processo de aprendizado a fase de maior consumo de tempo. O problema pode piorar em ferramentas de classificação interativas, o que pode ser inaceitável para grandes bases de dados. Um exemplo de classificador é o baseado em Floresta de Caminhos Ótimos [8] - OPF. Dado que muitos trabalhos tem sido orientados à implementação de algoritmos de reconhecimento de padrões em ambiente General Purpose Graphics Processing Unit - GPGPU, o presente estudo objetivou a implementação da etapa de treinamento do classificador Floresta de Caminhos Ótimos em CUDA, visando aumentar a sua eficiência. A otimização do classificador em CUDA demonstrou uma fase de treinamento mais rápida que a versão original.

Using GPUs for beamforming acceleration on SAFT imaging

SAFT techniques are based on the sequential activation, in emission and reception, of the array elements and the post-processing of all the received signals to compose the image. Thus, the image generation can be divided into two stages: (1) the excitation and acquisition stage, where the signals received by each element or group of elements are stored; and (2) the beamforming stage, where the signals are combined together to obtain the image pixels. The use of Graphics Processing Units (GPUs), which are programmable devices with a high level of parallelism, can accelerate the computations of the beamforming process, that usually includes different functions such as dynamic focusing, band-pass filtering, spatial filtering or envelope detection. This work shows that using GPU technology can accelerate, in more than one order of magnitude with respect to CPU implementations, the beamforming and post-processing algorithms in SAFT imaging. ©2009 IEEE.

Image Re-Ranking Acceleration on GPUs

Huge image collections are becoming available lately. In this scenario, the use of Content-Based Image Retrieval (CBIR) systems has emerged as a promising approach to support image searches. The objective of CBIR systems is to retrieve the most similar images in a collection, given a query image, by taking into account image visual properties such as texture, color, and shape. In these systems, the effectiveness of the retrieval process depends heavily on the accuracy of ranking approaches. Recently, re-ranking approaches have been proposed to improve the effectiveness of CBIR systems by taking into account the relationships among images. The re-ranking approaches consider the relationships among all images in a given dataset. These approaches typically demands a huge amount of computational power, which hampers its use in practical situations. On the other hand, these methods can be massively parallelized. In this paper, we propose to speedup the computation of the RL-Sim algorithm, a recently proposed image re-ranking approach, by using the computational power of Graphics Processing Units (GPU). GPUs are emerging as relatively inexpensive parallel processors that are becoming available on a wide range of computer systems. We address the image re-ranking performance challenges by proposing a parallel solution designed to fit the computational model of GPUs. We conducted an experimental evaluation considering different implementations and devices. Experimental results demonstrate that significant performance gains can be obtained. Our approach achieves speedups of 7x from serial implementation considering the overall algorithm and up to 36x on its core steps.

Implementação do algoritmo de Newman para identificação de comunidades em placas gráficas

Communities are present on physical, chemical and biological systems and their identification is fundamental for the comprehension of the behavior of these systems. Recently, available data related to complex networks have grown exponentially, demanding more computational power. The Graphical Processing Unit (GPU) is a cost effective alternative suitable for this purpose. We investigate the convenience of this for network science by proposing a GPU based implementation of Newman community detection algorithm. We showed that the processing time of matrix multiplications of GPUs grow slower than CPUs in relation to the matrix size. It was proven, thus, that GPU processing power is a viable solution for community dentification simulation that demand high computational power. Our implementation was tested on an integrated biological network for the bacterium Escherichia coli

Paralelização automática de laços

Identify opportunities for software parallelism is a task that takes a lot of human time, but once some code patterns for parallelism are identified, a software could quickly accomplish this task. Thus, automating this process brings many benefits such as saving time and reducing errors caused by the programmer [1]. This work aims at developing a software environment that identifies opportunities for parallelism in a source code written in C language, and generates a program with the same behavior, but with higher degree of parallelism, compatible with a graphics processor compatible with CUDA architecture.