Big Data: a big opportunity for the petroleum and petrochemical industry

The Petroleum and Petrochemical (P&P) industry is home to the most traded commodity in the world, i.e. oil. Recently, this industry has been struggling to make ends meet with top lines being affected by falling oil prices and bottom lines being squeezed further via increasing operational costs. It is against this backdrop that this paper seeks to identify and summarise the positive influence that the adoption of Big Data can have on the P&P industry. Exhaustive research is carried out on the industry’s engagement and adoption of Big Data in upstream, midstream and downstream operations to concisely summarise the varied applications and the potential benefits. Our research indicates that the upstream sector is actively engaging with Big Data to achieve efficiency gains while the midstream and downstream sectors are lagging behind. Overall, it is evident that the P&P industry can find solutions to its aching financial and productivity issues by embracing of Big Data

Numerical solutions of differential equations on FPGA-enhanced computers

Conventionally, to speed up scientific or engineering (S&E) computation programs on general-purpose computers, one may elect to use faster CPUs, more memory, systems with more efficient (though complicated) architecture, better software compilers, or even coding with assembly languages. With the emergence of Field Programmable Gate Array (FPGA) based Reconfigurable Computing (RC) technology, numerical scientists and engineers now have another option using FPGA devices as core components to address their computational problems. The hardware-programmable, low-cost, but powerful “FPGA-enhanced computer” has now become an attractive approach for many S&E applications. A new computer architecture model for FPGA-enhanced computer systems and its detailed hardware implementation are proposed for accelerating the solutions of computationally demanding and data intensive numerical PDE problems. New FPGAoptimized algorithms/methods for rapid executions of representative numerical methods such as Finite Difference Methods (FDM) and Finite Element Methods (FEM) are designed, analyzed, and implemented on it. Linear wave equations based on seismic data processing applications are adopted as the targeting PDE problems to demonstrate the effectiveness of this new computer model. Their sustained computational performances are compared with pure software programs operating on commodity CPUbased general-purpose computers. Quantitative analysis is performed from a hierarchical set of aspects as customized/extraordinary computer arithmetic or function units, compact but flexible system architecture and memory hierarchy, and hardwareoptimized numerical algorithms or methods that may be inappropriate for conventional general-purpose computers. The preferable property of in-system hardware reconfigurability of the new system is emphasized aiming at effectively accelerating the execution of complex multi-stage numerical applications. Methodologies for accelerating the targeting PDE problems as well as other numerical PDE problems, such as heat equations and Laplace equations utilizing programmable hardware resources are concluded, which imply the broad usage of the proposed FPGA-enhanced computers

Commodity clusters: performance comparison between PCs and workstations

Workstation clusters were originally developed as a way to leverage the better cost basis of UNIX workstations to perform computations previously handled only by relatively more expensive supercomputers. Commodity workstation clusters take this evolutionary process one step further by replacing equivalent proprietary workstation functionality with less expensive PC technology. As PC technology encroaches on proprietary UNIX workstation vendor markets, these vendors will see a declining share of the overall market. As technology advances continue, the ability to upgrade a workstations performance plays a large role in cost analysis. For example, a major upgrade to a typical UNIX workstation means replacing the whole machine. As major revisions to the UNIX vendor`s product line come out, brand new systems are introduced. IBM compatibles, however, are modular by design, and nothing need to be replaced except the components that are truly improved. The DAISy cluster, for example, is about to undergo a major upgrade from 90MHz Pentiums to 200MHz Pentium Pros. All of the memory -- the system`s largest expense -- and disks, power supply, etc., can be reused. As a result, commodity workstation clusters ought to gain an increasingly large share of the distributed computing market

3D seismic imaging through reverse-time migration on homogeneous and heterogeneous multi-core processors

Abstract. Reverse-Time Migration (RTM) is a state-of-the-art technique in seismic acoustic imaging, because of the quality and integrity of the images it provides. Oil and gas companies trust RTM with crucial decisions on multi-million-dollar drilling investments. But RTM requires vastly more computational power than its predecessor techniques, and this has somewhat hindered its practical success. On the other hand, despite multi-core architectures promise to deliver unprecedented computational power, little attention has been devoted to mapping efficiently RTM to multi-cores. In this paper, we present a mapping of the RTM computational kernel to the IBM Cell/B.E. processor that reaches close-tooptimal performance. The kernel proves to be memory-bound and it achieves a 98% utilization of the peak memory bandwidth. Our Cell/B.E. implementation outperforms a traditional processor (PowerPC 970MP) in terms of performance (with an 15.0× speedup) and energy-efficiency (with a 10.0× increase in the GFlops/W delivered). Also, it is the fastest RTM implementation available to the best of our knowledge. These results increase the practical usability of RTM. Also, the RTM-Cell/B.E. combination proves to be a strong competitor in the seismic arena

The 1st International Electronic Conference on Algorithms

This book presents 22 of the accepted presentations at the 1st International Electronic Conference on Algorithms which was held completely online from September 27 to October 10, 2021. It contains 16 proceeding papers as well as 6 extended abstracts. The works presented in the book cover a wide range of fields dealing with the development of algorithms. Many of contributions are related to machine learning, in particular deep learning. Another main focus among the contributions is on problems dealing with graphs and networks, e.g., in connection with evacuation planning problems

Abstracts of the 1st GeoDays, 14th–17th March 2023, Helsinki, Finland

Non peer reviewe

Heterogeneity, High Performance Computing, Self-Organization and the Cloud

application; blueprints; self-management; self-organisation; resource management; supply chain; big data; PaaS; Saas; HPCaa