Related papers: Parallel Seismic Data Processing Performance with …
Seismology has entered the petabyte era, driven by decades of continuous recordings of broadband networks, the increase in nodal seismic experiments, and the recent emergence of Distributed Acoustic Sensing (DAS). This review explains how…
Adapting the cloud for high-performance computing (HPC) is a challenging task, as software for HPC applications hinges on fast network connections and is sensitive to hardware failures. Using cloud infrastructure to recreate conventional…
Data processing systems are increasingly deployed in the cloud. While monolithic systems run fully on virtual servers, recent systems embrace cloud infrastructure and utilize the disaggregation of compute and storage to scale them…
In this paper, we draw the specifications of a novel benchmark for comparing parallel processing frameworks in the context of big data applications hosted in the cloud. We aim at filling several gaps in already existing cloud data…
Increasing amounts of data from varied sources, particularly in the fields of machine learning and graph analytics, are causing storage requirements to grow rapidly. A variety of technologies exist for storing and sharing these data,…
Parallel computing is very important to accelerate the performance of software systems. Additionally, considering that a recurring challenge is to process high data volumes continuously, stream processing emerged as a paradigm and software…
Several scientific and industry applications require High Performance Computing (HPC) resources to process and/or simulate complex models. Not long ago, companies, research institutes, and universities used to acquire and maintain…
Training and deploying deep learning models in real-world applications require processing large amounts of data. This is a challenging task when the amount of data grows to a hundred terabytes, or even, petabyte-scale. We introduce a hybrid…
This abstract presents a serverless approach to seismic imaging in the cloud based on high-throughput containerized batch processing, event-driven computations and a domain-specific language compiler for solving the underlying wave…
Data processing frameworks such as Apache Beam and Apache Spark are used for a wide range of applications, from logs analysis to data preparation for DNN training. It is thus unsurprising that there has been a large amount of work on…
Machine learning-based seismic processing models are typically trained separately to perform specific seismic processing tasks (SPTs), and as a result, require plenty of training data. However, preparing training data sets is not trivial,…
Distributed Computation has been a recent trend in engineering research. Parallel Computation is widely used in different areas of Data Mining, Image Processing, Simulating Models, Aerodynamics and so forth. One of the major usage of…
The amount of remote sensing data available to applications is constantly growing due to the rise of very-high-resolution sensors and short repeat cycle satellites. Consequently, tackling computational complexity in Earth Observation…
In the fields of computer vision (CV) and remote sensing (RS), foundational models typically follow the "big data + large model parameters" paradigm. However, the application of this strategy in seismic data processing faces several…
The Data Science domain has expanded monumentally in both research and industry communities during the past decade, predominantly owing to the Big Data revolution. Artificial Intelligence (AI) and Machine Learning (ML) are bringing more…
High intensive computation applications can usually take days to months to finish an execution. During this time, it is common to have variations of the available resources when considering that such hardware is usually shared among a…
Stream processing is a computing paradigm that supports real-time data processing for a wide variety of applications. At Meta, it's used across the company for various tasks such as deriving product insights, providing and improving user…
Cloud computing recently developed into a viable alternative to on-premises systems for executing high-performance computing (HPC) applications. With the emergence of new vendors and hardware options, there is now a growing need to…
We introduce the Seismic Waveforms dataset for Automatic Neural-network processing (SWAN), a comprehensive and standardized benchmark designed to advance data-driven seismic signal processing. SWAN aggregates diverse synthetic and real…
Cloud computing has become increasingly popular. Many options of cloud deployments are available. Testing cloud performance would enable us to choose a cloud deployment based on the requirements. In this paper, we present an innovative…