Parallel map projection of vector-based big spatial data: Coupling cloud computing with graphics processing units

2017 ◽  
Vol 61 ◽  
pp. 187-197 ◽  
Author(s):  
Wenwu Tang ◽  
Wenpeng Feng
Keyword(s):  
Cloud Computing ◽  
Spatial Data ◽  
Graphics Processing Units ◽  
Map Projection ◽  
Graphics Processing

scholarly journals Extending the usage of graphics processing units on the cloud for cost savings on seismic data regularization

2021 ◽  
Vol 38 (2) ◽  
Author(s):  
Nicholas Torres Okita ◽  
Tiago A. Coimbra ◽  
José Ribeiro ◽  
Martin Tygel
Keyword(s):  
Cloud Computing ◽  
Graphics Processing Units ◽  
Cost Savings ◽  
Data Sets ◽  
Computing Paradigm ◽  
Common Reflection Surface ◽  
User Demand ◽  
Computationally Intensive ◽  
Zero Offset ◽  
Graphics Processing

ABSTRACT. The usage of graphics processing units is already known as an alternative to traditional multi-core CPU processing, offering faster performance in the order of dozens of times in parallel tasks. Another new computing paradigm is cloud computing usage as a replacement to traditional in-house clusters, enabling seemingly unlimited computation power, no maintenance costs, and cutting-edge technology, dynamically on user demand. Previously those two tools were used to accelerate the estimation of Common Reflection Surface (CRS) traveltime parameters, both in zero-offset and finite-offset domain, delivering very satisfactory results with large time savings from GPU devices alongside cost savings on the cloud. This work extends those results by using GPUs on the cloud to accelerate the Offset Continuation Trajectory (OCT) traveltime parameter estimation. The results have shown that the time and cost savings from GPU devices’ usage are even larger than those seen in the CRS results, being up to fifty times faster and sixty times cheaper. This analysis reaffirms that it is possible to save both time and money when using GPU devices on the cloud and concludes that the larger the data sets are and the more computationally intensive the traveltime operators are, we can see larger improvements.Keywords: cloud computing, GPU, seismic processing. Estendendo o uso de placas gráficas na nuvem para economias em regularização de dados sísmicosRESUMO. O uso de aceleradores gráficos para processamento já é uma alternativa conhecida ao uso de CPUs multi-cores, oferecendo um desempenho na ordem de dezenas de vezes mais rápido em tarefas paralelas. Outro novo paradigma de computação é o uso da nuvem computacional como substituta para os tradicionais clusters internos, possibilitando o uso de um poder computacional aparentemente infinito sem custo de manutenção e com tecnologia de ponta, dinamicamente sob demanda de usuário. Anteriormente essas duas ferramentas foram utilizadas para acelerar a estimação de parâmetros do tempo de trânsito de Common Reflection Surface (CRS), tanto em zero-offset quanto em offsets finitos, obtendo resultados satisfatórios com amplas economias tanto de tempo quanto de dinheiro na nuvem. Este trabalho estende os resultados obtidos anteriormente, desta vez utilizando GPUs na nuvem para acelerar a estimação de parâmetros do tempo de trânsito em Offset Continuation Trajectory (OCT). Os resultados obtidos mostraram que as economias de tempo e dinheiro foram ainda maiores do que aquelas obtidas no CRS, sendo até cinquenta vezes mais rápido e sessenta vezes mais barato. Esta análise reafirma que é possível economizar tanto tempo quanto dinheiro usando GPUs na nuvem, e conclui que quanto maior for o dado e quanto mais computacionalmente intenso for o operador, maiores serão os ganhos de desempenho observados e economias.Palavras-chave: computação em nuvem, GPU, processamento sísmico. 

scholarly journals ELASTIC CLOUD COMPUTING ARCHITECTURE AND SYSTEM FOR HETEROGENEOUS SPATIOTEMPORAL COMPUTING

2017 ◽  
Vol IV-4/W2 ◽  
pp. 115-119
Author(s):  
X. Shi
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Graphics Processing Units ◽  
Heterogeneous Data ◽  
Data Types ◽  
Desktop Computer ◽  
Computing Architecture ◽  
Field Programmable ◽  
Computing Platforms ◽  
Graphics Processing

Spatiotemporal computation implements a variety of different algorithms. When big data are involved, desktop computer or standalone application may not be able to complete the computation task due to limited memory and computing power. Now that a variety of hardware accelerators and computing platforms are available to improve the performance of geocomputation, different algorithms may have different behavior on different computing infrastructure and platforms. Some are perfect for implementation on a cluster of graphics processing units (GPUs), while GPUs may not be useful on certain kind of spatiotemporal computation. This is the same situation in utilizing a cluster of Intel's many-integrated-core (MIC) or Xeon Phi, as well as Hadoop or Spark platforms, to handle big spatiotemporal data. Furthermore, considering the energy efficiency requirement in general computation, Field Programmable Gate Array (FPGA) may be a better solution for better energy efficiency when the performance of computation could be similar or better than GPUs and MICs. It is expected that an elastic cloud computing architecture and system that integrates all of GPUs, MICs, and FPGAs could be developed and deployed to support spatiotemporal computing over heterogeneous data types and computational problems.

Faster and cheaper: how graphics processing units on spot-market instances minimize turnaround time and budget

2020 ◽  
pp. 1-67
Author(s):  
Nicholas T. Okita ◽  
Tiago A. Coimbra
Keyword(s):  
Cloud Computing ◽  
Graphics Processing Units ◽  
High Performance ◽  
Imaging Techniques ◽  
Spot Market ◽  
Turnaround Time ◽  
On Demand ◽  
Order Of Magnitude ◽  
Graphics Processing ◽  
Price Differences

Cloud computing is enabling users to instantiate and access high-performance computing clusters quickly. However, without proper knowledge of the type of application and the nature of the instances, it can become quite expensive. The objective is to show that adequately choosing the instances provides a fast execution, which, in turn, leads to a low execution price, using the pay-as-you-go model on cloud computing. We used graphics processing units instances on the spot market to execute a seismic-dataset interpolation job and compared their performance to regular on-demand CPU instances. Furthermore, we explored how scaling could also improve the execution times at small price differences. The experiments have shown that, by using an instance with eight accelerators on the spot market, we obtain up to three hundred times speed-up compared to the on-demand CPU options, while being one hundred times cheaper. Finally, our results have shown that seismic-imaging processing can be sped up by order of magnitude with a low budget, resulting in faster and cheaper turn around processing time and enabling possible new imaging techniques.

scholarly journals Efficient Parallel Implementations of LWE-Based Post-Quantum Cryptosystems on Graphics Processing Units

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1781
Author(s):  
SangWoo An ◽  
Seog Chung Seo
Keyword(s):  
Cloud Computing ◽  
Parallel Processing ◽  
Graphics Processing Units ◽  
Optimization Techniques ◽  
Parallel Optimization ◽  
Processing Unit ◽  
Processing Technologies ◽  
Performance Improvements ◽  
Cloud Computing Service ◽  
Graphics Processing

With the development of the Internet of Things (IoT) and cloud computing technology, various cryptographic systems have been proposed to protect increasing personal information. Recently, Post-Quantum Cryptography (PQC) algorithms have been proposed to counter quantum algorithms that threaten public key cryptography. To efficiently use PQC in a server environment dealing with large amounts of data, optimization studies are required. In this paper, we present optimization methods for FrodoKEM and NewHope, which are the NIST PQC standardization round 2 competition algorithms in the Graphics Processing Unit (GPU) platform. For each algorithm, we present a part that can perform parallel processing of major operations with a large computational load using the characteristics of the GPU. In the case of FrodoKEM, we introduce parallel optimization techniques for matrix generation operations and matrix arithmetic operations such as addition and multiplication. In the case of NewHope, we present a parallel processing technique for polynomial-based operations. In the encryption process of FrodoKEM, the performance improvements have been confirmed up to 5.2, 5.75, and 6.47 times faster than the CPU implementation in FrodoKEM-640, FrodoKEM-976, and FrodoKEM-1344, respectively. In the encryption process of NewHope, the performance improvements have been shown up to 3.33 and 4.04 times faster than the CPU implementation in NewHope-512 and NewHope-1024, respectively. The results of this study can be used in the IoT devices server or cloud computing service server. In addition, the results of this study can be utilized in image processing technologies such as facial recognition technology.

Sign in / Sign up

Export Citation Format

Share Document