Jack Marquez

Jack Marquez

Research Assitant Professor @ UTK

Posts by Collection

portfolio

publications

Architecture for integrating real objects with virtual academic communities

Published in 2015 Fifth International Conference on e-Learning (econf), 2015

This paper is about the number 2. The number 3 is left for future work.

Recommended citation: V. J. A. Villanueva, F. J. D. Marquez, A. Z. M. Solarte and A. G. Dávalos, "Architecture for Integrating Real Objects with Virtual Academic Communities," 2015 Fifth International Conference on e-Learning (econf), Manama, Bahrain, 2015, pp. 385-391. doi: 10.1109/ECONF.2015.74 https://link.springer.com/chapter/10.1007/978-3-319-31232-3_19

IoT in education: Integration of objects with virtual academic communities

Published in New advances in information systems and technologies, 2016

The Internet of Things (IoT) is a new concept that allows objects to be connected to Internet. This connectivity allows the emergence of new forms of interaction between objects and people. In educational environments the IoT could be applied to improve teaching and learning experiences. This paper proposes a new architecture for integrating objects available in educational environments with virtual academic communities (VAC). This new architecture is based on the paradigm of layered architectures and architectural styles such as REST. The proposed architecture consists of four layers: hardware/communications, messaging, services, and application. Test of the proposed architecture were made through the implementation of a case study, which was focused on practical classes of a typical digital electronics course.

Recommended citation: Marquez, J., Villanueva, J., Garcia, A. and Solarte, Z., 2016. IoT in Education: Integration of Objects with Virtual Academic Communities. New Advances in Information Systems and Technologies vol.1 , 444, pp.201-212. https://ieeexplore.ieee.org/abstract/document/7478261/

Heterogeneity-aware data placement in Hybrid Clouds

Published in International Conference on Cloud Computing, 2019

In next-generation cloud computing clusters, performance of data-intensive applications will be limited, among other factors, by disks data transfer rates. In order to mitigate performance impacts, cloud systems offering hierarchical storage architectures are becoming commonplace. The Hadoop File System (HDFS) offers a collection of storage policies that exploit different storage types such as RAM_DISK, SSD, HDD, and ARCHIVE. However, developing algorithms to leverage heterogeneous storage through an efficient data placement has been challenging. This work presents an intelligent algorithm based on genetic programming which allow to find the optimal mapping of input datasets to storage types on a Hadoop file system.

Recommended citation: Marquez J.D., Gonzalez J.D., Mondragon O.H. (2019) Heterogeneity-Aware Data Placement in Hybrid Clouds. In: Da Silva D., Wang Q., Zhang LJ. (eds) Cloud Computing – CLOUD 2019. CLOUD 2019. Lecture Notes in Computer Science, vol 11513. Springer https://link.springer.com/chapter/10.1007/978-3-030-23502-4_13

Performance comparison: Virtual machines and containers running artificial intelligence applications

Published in International Conference on Information Technology & Systems, 2021

With the continuous growth of data that can be valuable for companies and scientific research, cloud computing has shown itself as one of the emerging technologies that can help solve many of these applications that need the right level of computing and ubiquitous access to them. Cloud Computing has a base technology that is virtualization, which has evolved to provide users with features from which they can benefit. There are different types of virtualization and each of them has its own way of carrying out some processes and of managing computational resources. In this paper, we present the comparison of performance between virtual machines and containers, specifically between an instance of OpenStack and docker and singularity containers. The application used to measure performance is a real application of artificial intelligence. We present the obtained results and discuss them.

Recommended citation: Marquez J.D., Castillo M. (2021) Performance Comparison: Virtual Machines and Containers Running Artificial Intelligence Applications. In: Rocha Á., Ferrás C., López- López P.C., Guarda T. (eds) Information Technology and Systems. ICITS 2021. Advances in Intelligent Systems and Computing, vol 1330. Springer https://link.springer.com/chapter/10.1007/978-3-030-68285-9_20

An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures

Published in Applied Sciences, 2021

Cloud computing systems are rapidly evolving toward multicloud architectures supported on heterogeneous hardware. Cloud service providers are widely offering different types of storage infrastructures and multi-NUMA architecture servers. Existing cloud resource allocation solutions do not comprehensively consider this heterogeneous infrastructure. In this study, we present a novel approach comprised of a hierarchical framework based on genetic programming to solve problems related to data placement and virtual machine allocation for analytics applications running on heterogeneous hardware with a variety of storage types and nonuniform memory access. Our approach optimizes data placement using the Hadoop File System on heterogeneous storage devices on multicloud systems. It guarantees the efficient allocation of virtual machines on physical machines with multiple NUMA (nonuniform memory access) domains by minimizing contention between workloads. We prove that our solutions for data placement and virtual machine allocation outperform other state-of-the-art approaches.

Recommended citation: Marquez, J.; Mondragon, O.H.; Gonzalez, J.D. An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures. Appl. Sci. 2021, 11, 9940. https://doi.org/10.3390/app11219940 https://www.mdpi.com/2076-3417/11/21/9940/pdf

Computational and Communication Infrastructure Challenges for Resilient Cloud Services

Published in Computers, 2022

Fault tolerance and the availability of applications, computing infrastructure, and communications systems during unexpected events are critical in cloud environments. The microservices architecture, and the technologies that it uses, should be able to maintain acceptable service levels in the face of adverse circumstances. In this paper, we discuss the challenges faced by cloud infrastructure in relation to providing resilience to applications. Based on this analysis, we present our approach for a software platform based on a microservices architecture, as well as the resilience mechanisms to mitigate the impact of infrastructure failures on the availability of applications. We demonstrate the capacity of our platform to provide resilience to analytics applications, minimizing service interruptions and keeping acceptable response times

Recommended citation: Martinez, H. F., Mondragon, O. H., Rubio, H. A., & Marquez, J. (2022). Computational and Communication Infrastructure Challenges for Resilient Cloud Services. Computers, 11(8), 118. https://www.mdpi.com/2073-431X/11/8/118/pdf?version=1659104455

Runtime Steering of Molecular Dynamics Simulations Through In Situ Analysis and Annotation of Collective Variables

Published in PASC '23: Proceedings of the Platform for Advanced Scientific Computing Conference, 2023

This paper targets one of the most common simulations on petascale and, very likely, on exascale machines: molecular dynamics (MD) simulations studying the (classical) time evolution of a molecular system at atomic resolution. Specifically, this work addresses the data challenges of MD simulations at exascale through (1) the creation of a data analysis method based on a suite of advanced collective variables (CVs) selected for annotation of structural molecular properties and capturing rare conformational events at runtime, (2) the definition of an in situ framework to automatically identify the frames where the rare events occur during an MD simulation and (3) the integration of both method and framework into two MD workflows for the study of early termination or termination and restart of a benchmark molecular system for protein folding —the Fs peptide system (Ace-A_5(AAARA)_3A-NME)— using Summit. The approach achieves faster exploration of the conformational space compared to extensive ensemble simulations. Specifically, our in situ framework with early termination alone achieves 99.6% coverage of the reference conformational space for the Fs peptide with just 60% of the MD steps otherwise used for a traditional execution of the MD simulation. Annotation-based restart allows us to cover 94.6% of the conformational space, just running 50% of the overall MD steps.

Recommended citation: Caino-Lores, S., Cuendet, M., Marquez, J., Kots, E., Estrada, T., Deelman, E., ... & Taufer, M. (2023, June). Runtime Steering of Molecular Dynamics Simulations Through In Situ Analysis and Annotation of Collective Variables. In Proceedings of the Platform for Advanced Scientific Computing Conference (pp. 1-11). https://dl.acm.org/doi/pdf/10.1145/3592979.3593420

Scalable Incremental Checkpointing using GPU-Accelerated De-Duplication

Published in ICPP'23: 52nd International Conference on Parallel Processing, 2023

Writing large amounts of data concurrently to stable storage is a typical I/O pattern of many HPC workflows. This pattern introduces high I/O overheads and results in increased storage space utilization especially for workflows that need to capture the evolution of data structures with high frequency as checkpoints. In this context, many applications, such as graph pattern matching, perform sparse updates to large data structures between checkpoints. For these applications, incremental checkpointing techniques that save only the differences from one checkpoint to another can dramatically reduce the checkpoint sizes, I/O bottlenecks, and storage space utilization. However, such techniques are not without challenges: it is non-trivial to transparently determine what data has changed since a previous checkpoint and assemble the differences in a compact fashion that does not result in excessive metadata. State-of-art data reduction techniques (e.g., compression and de-duplication) have significant limitations when applied to modern HPC applications that leverage GPUs: slow at detecting the differences, generate a large amount of metadata to keep track of the differences, and ignore crucial spatiotemporal checkpoint data redundancy. This paper addresses these challenges by proposing a Merkle tree-based incremental checkpointing method to exploit GPUs' high memory bandwidth and massive parallelism. Experimental results at scale show a significant reduction of the I/O overhead and space utilization of checkpointing compared with state-of-the-art incremental checkpointing and compression techniques.

Recommended citation: Tan, N., Luettgau, J., Marquez, J., Terianishi, K., Morales, N., Bhowmick, S., ... & Nicolae, B. (2023, August). Scalable Incremental Checkpointing using GPU-Accelerated De-Duplication. In ICPP'23: 52nd International Conference on Parallel Processing. https://hal.science/hal-04173764/document

Online Boosted Gaussian Learners for In-Situ Detection and Characterization of Protein Folding States in Molecular Dynamics Simulations

Published in 2023 IEEE 19th International Conference on e-Science (e-Science), 2023

Links

Recommended citation: Harshita Sahni, Hector Carrillo-Cabada, Ekaterina Kots, Silvina Caino-Lores, Jack Marquez, Ewa Deelman, Michel Cuendet, Harel Weinstein, Michela Taufer, Trilce Estrada. (2023). Online Boosted Gaussian Learners for In-Situ Detection and Characterization of Protein Folding States in Molecular Dynamics Simulations. 2023 IEEE 19th International Conference on e-Science (e-Science). doi:10.1109/e-science58273.2023.10254895 https://doi.org/10.1109/e-science58273.2023.10254895

EduHPC Lightning Talk Summary

Published in Proceedings of the SC '23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, 2023

Links

Recommended citation: Michael Alexander, Sanjukta Bhowmick, Befikir Bogale, Gilberto Diaz, Anne C. Elster, Danielle A. Ellsworth, Carlos Jaime Barrios Hernandez, Evan Jaffe, Jack Marquez, Alison Melton, et al.. (2023). EduHPC Lightning Talk Summary. Proceedings of the SC '23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis. doi:10.1145/3624062.3625542 https://doi.org/10.1145/3624062.3625542

Modelling the Impact of Cloud Storage Heterogeneity on HPC Application Performance

Published in Computation, 2024

Moving high-performance computing (HPC) applications from HPC clusters to cloud computing clusters, also known as the HPC cloud, has recently been proposed by the HPC research community. Migrating these applications from the former environment to the latter can have an important impact on their performance, due to the different technologies used and the suboptimal use and configuration of cloud resources such as heterogeneous storage. Probabilistic models can be applied to predict the performance of these applications and to optimise them for the new system. Modelling the performance in the HPC cloud of applications that use heterogeneous storage is a difficult task, due to the variations in performance. This paper presents a novel model based on Extreme Value Theory (EVT) for the analysis, characterisation and prediction of the performance of HPC applications that use heterogeneous storage technologies in the cloud and high-performance distributed parallel file systems. Unlike standard approaches, our model focuses on extreme values, capturing the true variability and potential bottlenecks in storage performance. Our model is validated using return level analysis to study the performance of representative scientific benchmarks running on heterogeneous cloud storage at a large scale and gives prediction errors of less than 7%.

Recommended citation: Jack Marquez, Oscar H. Mondragon. (2024). Modelling the Impact of Cloud Storage Heterogeneity on HPC Application Performance. Computation. doi:10.3390/computation12070150 https://doi.org/10.3390/computation12070150

Increasing the Efficiency of Ensemble Molecular Dynamics Simulations with Termination of Unproductive Trajectories Identified at Runtime

Published in The Journal of Physical Chemistry A, 2025

Links

Recommended citation: Jack Marquez, Michel A. Cuendet, Silvina Caino-Lores, Trilce Estrada, Ewa Deelman, Harel Weinstein, Michela Taufer. (2025). Increasing the Efficiency of Ensemble Molecular Dynamics Simulations with Termination of Unproductive Trajectories Identified at Runtime. The Journal of Physical Chemistry A. doi:10.1021/acs.jpca.4c05182 https://doi.org/10.1021/acs.jpca.4c05182

Advancing the GEOtiled Framework Through Scalable Terrain Parameter Computation

Published in Proceedings of the 34th International Symposium on High-Performance Parallel and Distributed Computing, 2025

Links

Recommended citation: Gabriel Laboy, Paula Olaya, Jack Marquez, Michael Sutherlin, Rodrigo Vargas, Michela Taufer. (2025). Advancing the GEOtiled Framework Through Scalable Terrain Parameter Computation. Proceedings of the 34th International Symposium on High-Performance Parallel and Distributed Computing. doi:10.1145/3731545.3735117 https://doi.org/10.1145/3731545.3735117

Application of graph alignment techniques for identifying sources of non-determinism in MPI simulations

Published in The International Journal of High Performance Computing Applications, 2026

Scientific high performance computing (HPC) applications employ asynchronous executions of MPI calls to improve scalability and performance. The asynchronous calls can lead to non-determinism (ND) in execution, particularly for large exascale simulations. In order to ensure reproducibility and facilitate error detection, it is imperative to identify the sources of non-determinism. Message ND that occurs when the order in which a process sends or receives MPI communication, or executes MPI calls varies across different runs of the same application. We model the MPI calls in the execution as an event graph. The regions of dissimilarity between two event graphs indicate the sources of non-determinism in the MPI calls. Thus by aligning the nodes of the event graphs, we can identify sources of ND. We show that traditional alignment techniques such as NetAlign and learning methodologies such as Graph Autoencoders are not able to align graphs with high accuracy due to the nearly regular degree and large diameter of event graphs. Therefore, we propose a meta graph heuristic that exploits structural properties of event graphs, by combining the set of nodes representing sequences of MPI calls within the same processor as a meta node. We align the meta graphs formed from these meta nodes, and then align the individual nodes within the meta nodes. Our results over three different MPI applications highlight that our meta graph heuristic has better accuracy and scales to large graphs compared to network alignment and graph auto encder methods.

Recommended citation: Dhroov Pandey, Jack Marquez, Michela Taufer, Sanjukta Bhowmick. (2026). Application of graph alignment techniques for identifying sources of non-determinism in MPI simulations. The International Journal of High Performance Computing Applications. doi:10.1177/10943420251398118 https://doi.org/10.1177/10943420251398118

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.