Suren Byna
YOU?
Author Swipe
View article: CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning
CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning Open
Privacy-Preserving Federated Learning (PPFL) is a decentralized machine learning approach where multiple clients train a model collaboratively. PPFL preserves the privacy and security of a client's data without exchanging it. However, ensu…
View article: AIDRIN 2.0: A Framework to Assess Data Readiness for AI
AIDRIN 2.0: A Framework to Assess Data Readiness for AI Open
AI Data Readiness Inspector (AIDRIN) is a framework to evaluate and improve data preparedness for AI applications. It addresses critical data readiness dimensions such as data quality, bias, fairness, and privacy. This paper details enhanc…
View article: Regen: An object layout regenerator on large-scale production HPC systems
Regen: An object layout regenerator on large-scale production HPC systems Open
This article proposes an object layout regenerator called Regen which regenerates and removes the object layout dynamically to improve the read performance of applications. Regen first detects frequent access patterns from the I/O requests…
View article: I/O in Machine Learning Applications on HPC Systems: A 360-degree Survey
I/O in Machine Learning Applications on HPC Systems: A 360-degree Survey Open
Growing interest in Artificial Intelligence (AI) has resulted in a surge in demand for faster methods of Machine Learning (ML) model training and inference. This demand for speed has prompted the use of high performance computing (HPC) sys…
View article: Data Readiness for AI: A 360-Degree Survey
Data Readiness for AI: A 360-Degree Survey Open
Artificial Intelligence (AI) applications critically depend on data. Poor-quality data produces inaccurate and ineffective AI models that may lead to incorrect or unsafe use. Evaluation of data readiness is a crucial step in improving the …
View article: Parallel I/O Characterization and Optimization on Large-Scale HPC Systems: A 360-Degree Survey
Parallel I/O Characterization and Optimization on Large-Scale HPC Systems: A 360-Degree Survey Open
Driven by artificial intelligence, data science, and high-resolution simulations, I/O workloads and hardware on high-performance computing (HPC) systems have become increasingly complex. This complexity can lead to large I/O overheads and …
View article: TensorSearch: Parallel Similarity Search on Tensors
TensorSearch: Parallel Similarity Search on Tensors Open
Existing similarity search methods, often limited to scalar or vector data, struggle to identify complex patterns found in scientific datasets, such as 2D seismic events or 3D magnetic flux ropes. We introduce TensorSearch, a novel paralle…
View article: HDF5 in the exascale era: Delivering efficient and scalable parallel I/O for exascale applications
HDF5 in the exascale era: Delivering efficient and scalable parallel I/O for exascale applications Open
Accurately modeling real-world systems requires scientific applications at exascale to generate massive amounts of data and manage data storage efficiently. However, parallel input and output (I/O) faces challenges due to new application w…
View article: Object-Centric Data Management in HPC Workflows - A Case Study
Object-Centric Data Management in HPC Workflows - A Case Study Open
HPC workflows consist of multiple phases and components executed collaboratively to reach the same goal. They perform necessary computations and exchange data, of-ten through system-wide POSIX-compliant parallel file systems. However, POSI…
View article: AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI
AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI Open
Garbage In Garbage Out is a universally agreed quote by computer scientists from various domains, including Artificial Intelligence (AI). As data is the fuel for AI, models trained on low-quality, biased data are often ineffective. Compute…
View article: Proceedings of the 36th International Conference on Scientific and Statistical Database Management
Proceedings of the 36th International Conference on Scientific and Statistical Database Management Open
International audience
View article: ION: Navigating the HPC I/O Optimization Journey using Large Language Models
ION: Navigating the HPC I/O Optimization Journey using Large Language Models Open
Effectively leveraging the complex software and hardware I/O stacks of HPC systems to deliver needed I/O performance has been a challenging task for domain scientists. To identify and address I/O issues in their applications, scientists la…
View article: AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI
AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI Open
"Garbage In Garbage Out" is a universally agreed quote by computer scientists from various domains, including Artificial Intelligence (AI). As data is the fuel for AI, models trained on low-quality, biased data are often ineffective. Compu…
View article: Drilling Down I/O Bottlenecks with Cross-layer I/O Profile Exploration
Drilling Down I/O Bottlenecks with Cross-layer I/O Profile Exploration Open
I/O performance monitoring tools such as Darshan and Recorder collect I/O-related metrics on production systems and help understand the applications' behavior. However, some gaps prevent end-users from seeing the whole picture when it come…
View article: A2FL: Autonomous and Adaptive File Layout in HPC through Real-time Access Pattern Analysis
A2FL: Autonomous and Adaptive File Layout in HPC through Real-time Access Pattern Analysis Open
Various scientific applications with different I/O characteristics are executed in HPC systems. However, underlying parallel file systems are unaware of these characteristics of applications, and using a single fixed file layout for all ap…
View article: TunIO: An AI-powered Framework for Optimizing HPC I/O
TunIO: An AI-powered Framework for Optimizing HPC I/O Open
I/O operations are a known performance bottleneck of HPC applications. To achieve good performance, users often employ an iterative multistage tuning process to find an optimal I/O stack configuration. However, an I/O stack contains multip…
View article: I/O in Machine Learning Applications on HPC Systems: A 360-degree Survey
I/O in Machine Learning Applications on HPC Systems: A 360-degree Survey Open
Growing interest in Artificial Intelligence (AI) has resulted in a surge in demand for faster methods of Machine Learning (ML) model training and inference. This demand for speed has prompted the use of high performance computing (HPC) sys…
View article: h5bench: A unified benchmark suite for evaluating HDF5 I/O performance on pre‐exascale platforms
h5bench: A unified benchmark suite for evaluating HDF5 I/O performance on pre‐exascale platforms Open
Summary Parallel I/O is a critical technique for moving data between compute and storage subsystems of supercomputers. With massive amounts of data produced or consumed by compute nodes, high‐performant parallel I/O is essential. I/O bench…
View article: Data Readiness for AI: A 360-Degree Survey
Data Readiness for AI: A 360-Degree Survey Open
Artificial Intelligence (AI) applications critically depend on data. Poor quality data produces inaccurate and ineffective AI models that may lead to incorrect or unsafe use. Evaluation of data readiness is a crucial step in improving the …
View article: Parallel IO Libraries for Managing HEP Experimental Data
Parallel IO Libraries for Managing HEP Experimental Data Open
The computing and storage requirements of the energy and intensity frontiers will grow significantly during the Run 4 & 5 and the HL-LHC era. Similarly, in the intensity frontier, with larger trig ger readouts during supernovae explosions,…
View article: AIIO: Using Artificial Intelligence for Job-Level and Automatic I/O Performance Bottleneck Diagnosis
AIIO: Using Artificial Intelligence for Job-Level and Automatic I/O Performance Bottleneck Diagnosis Open
Manually diagnosing the I/O performance bottleneck for a single application (hereinafter referred to as the "job level'') is a tedious and error-prone procedure requiring domain scientists to have deep knowledge of complex storage systems.…
View article: PROV-IO+: A Cross-Platform Provenance Framework for Scientific Data on HPC Systems
PROV-IO+: A Cross-Platform Provenance Framework for Scientific Data on HPC Systems Open
Data provenance, or data lineage, describes the life cycle of data. In scientific workflows on HPC systems, scientists often seek diverse provenance (e.g., origins of data products, usage patterns of datasets). Unfortunately, existing prov…
View article: I/O Access Patterns in HPC Applications: A 360-Degree Survey
I/O Access Patterns in HPC Applications: A 360-Degree Survey Open
The high-performance computing I/O stack has been complex due to multiple software layers, the inter-dependencies among these layers, and the different performance tuning options for each layer. In this complex stack, the definition of an …
View article: Efficient Asynchronous I/O with Request Merging
Efficient Asynchronous I/O with Request Merging Open
With the advancement of exascale computing, the amount of scientific data is increasing day by day. Efficient data access is necessary for scientific discoveries. Unfortunately, the I/O performance is not improved, like the CPU and network…
View article: AIIO v0.0.1
AIIO v0.0.1 Open
AIIO uses Artificial Intelligence for Job-Level and Automatic I/O Performance Bottleneck Diagnosis.
View article: Accelerating Parallel Write via Deeply Integrating Predictive Lossy Compression with HDF5
Accelerating Parallel Write via Deeply Integrating Predictive Lossy Compression with HDF5 Open
Lossy compression is one of the most efficient solutions to reduce storage overhead and improve I/O performance for HPC applications. However, existing parallel I/O libraries cannot fully utilize lossy compression to accelerate parallel wr…