Ghaleb Abdulla - Academia.edu (original) (raw)

Papers by Ghaleb Abdulla

Zenodo (CERN European Organization for Nuclear Research), Jul 2, 2020

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2019

In the exascale era, HPC systems are expected to operate under different system-wide power-constr... more In the exascale era, HPC systems are expected to operate under different system-wide power-constraints. For such power-constrained systems, improving per-job flops-per-watt may not be sufficient to improve the total HPC productivity as more number of scientific applications with different compute intensities are migrating to the HPC systems. To measure HPC productivity for such applications, we utilize a monotonically decreasing time-dependent value function, called job-value, with each application. A job-value function represents the value of completing a job for an organization. We begin by exploring the trade-off between two commonly used static power allocation strategies (uniform and greedy) in a power-constrained oversubscribed system. We simulate a large-scale system and demonstrate that, at the tightest power constraint, the greedy allocation can lead to 30% higher productivity compared to the uniform allocation whereas, the uniform allocation can gain up to 6% higher productivity at the relaxed power constraint. We then propose a new dynamic power allocation strategy that utilizes power-performance models derived from offline data. We use these models for reallocating power from running jobs to newly arrived jobs to increase overall system utilization and productivity. In our simulation study, we show that compared to static allocation, the dynamic power allocation policy improves node utilization and job completion rates by 20% and 9%, respectively, at the tightest power constraint. Our dynamic approach consistently earns up to 8% higher productivity compared to the best performing static strategy under different power constraints.

IEEE Transactions on Parallel and Distributed Systems, 2020

Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region

AGU Fall Meeting Abstracts, Dec 1, 2020

Data management is the organization of information to support efficient access and analysis. For ... more Data management is the organization of information to support efficient access and analysis. For data intensive computing applications, the speed at which relevant data can be accessed is a limiting factor in terms of the size and complexity of computation that can be performed. Data access speed is impacted by the size of the relevant subset of the data, the complexity of the query used to define it, and the layout of the data relative to the query. As the underlying data sets become increasingly complex, the questions asked of it become more involved as well. For example, geospatial data associated with a city is no longer limited to the map data representing its streets, but now also includes layers identifying utility lines, key points, locations and types of businesses within the city limits, tax information for each land parcel, satellite imagery, and possibly even street-level views. As a result, queries have gone from simple questions, such as “how long is Main Street?”, to ...

Scientific experiments typically produce a plethora of files in the form of intermediate data or ... more Scientific experiments typically produce a plethora of files in the form of intermediate data or experimental results. As the project grows in scale, there is an increased need for tools and techniques that link together relevant experimental artifacts, especially if the files are heterogeneous and distributed across multiple locations. Current provenance and search techniques, however, fall short in efficiently retrieving experiment-related files, presumably because they are not tailored towards the common use cases of researchers. In this position paper, we propose Experiment Explorer, a lightweight and efficient approach that takes advantage of metadata to retrieve and visualize relevant experiment-related files.

High-Speed Networking and Multimedia Computing, 1994

ABSTRACT

Zenodo (CERN European Organization for Nuclear Research), Jul 2, 2020

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2019

In the exascale era, HPC systems are expected to operate under different system-wide power-constr... more In the exascale era, HPC systems are expected to operate under different system-wide power-constraints. For such power-constrained systems, improving per-job flops-per-watt may not be sufficient to improve the total HPC productivity as more number of scientific applications with different compute intensities are migrating to the HPC systems. To measure HPC productivity for such applications, we utilize a monotonically decreasing time-dependent value function, called job-value, with each application. A job-value function represents the value of completing a job for an organization. We begin by exploring the trade-off between two commonly used static power allocation strategies (uniform and greedy) in a power-constrained oversubscribed system. We simulate a large-scale system and demonstrate that, at the tightest power constraint, the greedy allocation can lead to 30% higher productivity compared to the uniform allocation whereas, the uniform allocation can gain up to 6% higher productivity at the relaxed power constraint. We then propose a new dynamic power allocation strategy that utilizes power-performance models derived from offline data. We use these models for reallocating power from running jobs to newly arrived jobs to increase overall system utilization and productivity. In our simulation study, we show that compared to static allocation, the dynamic power allocation policy improves node utilization and job completion rates by 20% and 9%, respectively, at the tightest power constraint. Our dynamic approach consistently earns up to 8% higher productivity compared to the best performing static strategy under different power constraints.

IEEE Transactions on Parallel and Distributed Systems, 2020

Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region

AGU Fall Meeting Abstracts, Dec 1, 2020

Data management is the organization of information to support efficient access and analysis. For ... more Data management is the organization of information to support efficient access and analysis. For data intensive computing applications, the speed at which relevant data can be accessed is a limiting factor in terms of the size and complexity of computation that can be performed. Data access speed is impacted by the size of the relevant subset of the data, the complexity of the query used to define it, and the layout of the data relative to the query. As the underlying data sets become increasingly complex, the questions asked of it become more involved as well. For example, geospatial data associated with a city is no longer limited to the map data representing its streets, but now also includes layers identifying utility lines, key points, locations and types of businesses within the city limits, tax information for each land parcel, satellite imagery, and possibly even street-level views. As a result, queries have gone from simple questions, such as “how long is Main Street?”, to ...

Scientific experiments typically produce a plethora of files in the form of intermediate data or ... more Scientific experiments typically produce a plethora of files in the form of intermediate data or experimental results. As the project grows in scale, there is an increased need for tools and techniques that link together relevant experimental artifacts, especially if the files are heterogeneous and distributed across multiple locations. Current provenance and search techniques, however, fall short in efficiently retrieving experiment-related files, presumably because they are not tailored towards the common use cases of researchers. In this position paper, we propose Experiment Explorer, a lightweight and efficient approach that takes advantage of metadata to retrieve and visualize relevant experiment-related files.

High-Speed Networking and Multimedia Computing, 1994

ABSTRACT