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All sources NASA NTRS DOE OSTI

DOE OSTI2021

I/O Bottleneck Detection and Tuning: Connecting the Dots using Interactive Log Analysis

Using parallel file systems efficiently is a tricky problem due to inter-dependencies among multiple layers of I/O software, including high-level I/O libraries (HDF5, netCDF, etc.), MPI-IO, POSIX, and file systems (GPFS, Lustre, etc.). Profiling tools such as Darshan collect traces to help understand the I/O performance behavior. However, there are significant gaps in analyzing the collected traces and then applying tuning options offered by various layers of I/O software. Seeking to connect the dots between I/O bottleneck detection and tuning, we propose DXT Explorer, an interactive log analysis tool. In this paper, we present a case study using our interactive log analysis tool to identify and apply various I/O optimizations. We report an evaluation of performance improvement achieved for four I/O kernels extracted from science applications.

Bez, Jean Luca↗

DOE OSTI2024

Drilling Down I/O Bottlenecks with Cross-layer I/O Profile Exploration

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 comes to detecting and drilling down to the root causes of I/O performance slowdowns and where those problems originate. These gaps arise from limitations in the available metrics, their collection strategy, and the lack of translation to actionable items that could advise on optimizations. This paper highlights such gaps and proposes solutions to drill down to the source code level to pinpoint the root causes of I/O bottlenecks scientific applications face by relying on cross-layer analysis combining multiple performance metrics related to I/O software layers. We demonstrate with two real applications how metrics collected in high-level libraries (which are closer to the data models used by an application), enhanced by source-code insights and natural language translations, can help streamline the understanding of I/O behavior and provide guidance to end-users, developers, and supercomputing facilities on how to improve I/O performance. Using this cross-layer analysis and the heuristic recommendations, we attained up to 6.9× speedup from run-as-is executions.

Ather, Hammad↗

DOE OSTI2025

Leveraging Pre-Built Catalogs and Object-Level Scheduling to Eliminate I/O Bottlenecks in HPC Environments

Modern High-Performance Computing (HPC) environments face mounting challenges due to the shift from large to small file datasets, along with an increasing number of users and parallelized applications. As HPC systems rely on Parallel File Systems (PFS), such as Lustre for data processing, performance bottlenecks stemming from Object Storage Target (OST) contention have become a significant concern. Existing solutions, such as LADS with its object-level scheduling approach, fall short in large-scale HPC environments due to their inability to effectively address metadata I/O bottlenecks and the growing number of I/O processes. This study highlights the pressing need for a comprehensive solution that tackles both OST contention and metadata I/O challenges in diverse HPC workloads. To address these challenges, we propose SwiftLoad, an object-level I/O scheduling framework that leverages a metadata catalog to enhance the performance and efficiency of parallel HPC utilities. The adoption of the metadata catalog mitigates the metadata I/O bottlenecks that commonly occur in HPC utilities, a challenge that is particularly pronounced in object-level I/O scheduling. SwiftLoad addresses OST contention and the uneven distribution of I/O processes across different OSTs through mathematical modeling and incorporates a Loader Configuration Module to regulate the number of I/O processes. Evaluated with two representative utilities—data deduplication profiling and data augmentation—SwiftLoad achieved performance improvements of up to 5.63x and 11.0x, respectively, on a production supercomputer.

HPC↗

DOE OSTI2022

Hvac: Removing I/O Bottleneck for Large-Scale Deep Learning Applications

Scientific communities are increasingly adopting deep learning (DL) models in their applications to accelerate scientific discovery processes. However, with rapid growth in the computing capabilities of HPC supercomputers, large-scale DL applications have to spend a significant portion of training time performing I/O to a parallel storage system. Previous research works have investigated optimization techniques such as prefetching and caching. Unfortunately, there exist non-trivial challenges to adopting the existing solutions on HPC supercomputers for large-scale DL training applications, which include non-performance and/or failures at extreme scale, lack of portability and generality in design, complex deployment methodology, and being limited to a specific application or dataset. To address these challenges, we propose High-Velocity AI Cache (HVAC), a distributed read-cache layer that targets and fully exploits the node-local storage or near node-local storage technology. HVAC seamlessly accelerates read I/O by aggregating node-local or near node-local storage, avoiding metadata lookups and file locking while preserving portability in the application code. We deploy and evaluate HVAC on 1,024 nodes (with over 6000 NVIDIA V100 GPUS) of the Summit supercomputer. In particular, we evaluate the scalability, efficiency, accuracy, and load distribution of HVAC compared to GPFS and XFS-on-NVMe. With four different DL applications, we observe an average 25 % performance improvement atop GPFS and 9% drop against XFS-on-NVMe, which scale linearly and are considered the performance upper bound. We envision HVAC as an important caching library for upcoming HPC supercomputers such as Frontier.

Khan, Awais↗

DOE OSTI2026

WisIO: Automated I/O Bottleneck Detection with Multi-Perspective Views for HPC Workflows

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Yildirim, Izzet↗

DOE OSTI2023

AIIO: Using Artificial Intelligence for Job-Level and Automatic I/O Performance Bottleneck Diagnosis

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. However, existing automatic methods for I/O performance bottleneck diagnosis have one major issue: the granularity of the analysis is at the platform or group level and the diagnosis results cannot be applied to the individual application. To address this issue, we designed and developed a method named "Artificial Intelligence for I/O"(AIIO), which uses AI and its interpretation technology to diagnose I/O performance bottlenecks at the job level automatically. By considering the sparsity of I/O log files, employing multiple AI models for performance prediction, merging diagnosis results across multiple models, and generalizing its performance prediction and diagnosis functions, AIIO can accurately and robustly identify the bottleneck of an even unseen application. Experimental results show that real and unseen applications can use the diagnosis results from AIIO to improve their I/O performance by at most 146 times.

Dong, Bin↗

DOE OSTI2023

Efficient Asynchronous I/O with Request Merging

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 speed. So, I/O operations take longer time than data generation or analysis. Asynchronous I/O has been proposed to extenuate the I/O bottleneck by overlapping I/O and computation time. However, multiple small write operations can diminish the benefits of asynchronous I/O, as the I/O time becomes significantly longer than the compute time, with little time to overlap with. To overcome these issues, we present an optimization technique to merge small contiguous write operations. We integrated our solution into the HDF5 asynchronous I/O VOL connector and demonstrated the effectiveness of merging HDF5 write operations automatically and transparently without requiring any code change from the application.

Chowdhury, Kamal Hossain↗

DOE OSTI2023

Scalable Incremental Checkpointing using GPU-Accelerated De-Duplication

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.

Tan, Nigel↗

DOE OSTI2019

NoC-enabled Software/Hardware Co-Design Framework for Accelerating k-mer Counting

Counting k-mers (substrings of fixed length k) in DNA and protein sequences generate non-uniform and irregular memory access patterns. Processing-in-Memory (PIM) architectures have potential to significantly reduce the overheads associated with such frequent and irregular memory accesses. However, existing k-mer counting algorithms are not designed to exploit the advantages of PIM architectures. Furthermore, owing to thermal constraints, the allowable power budget is limited in conventional PIM designs. Moreover, k-mer counting generates long-range, non-uniform traffic patterns which needs to be handled by an efficient Network-on-Chip (NoC). In this paper, we present an NoC-enabled hardware/software co-design framework to implement high-performance k-mer counting. The proposed architecture enables more computational power, efficient communication between cores/memory – all without creating a thermal bottleneck. The software component overcomes the I/O bottleneck, aids in NoC design and exposes more in-memory opportunities to exploit in PIM. Experimental results show that the proposed architecture outperforms a state-of-the-art software implementation using Hybrid Memory Cube (HMC), by up to 7.14X, while allowing significantly higher power budget.

Joardar, Biresh K.↗

DOE OSTI2020

Efficient Data Management in Neutron Scattering Data Reduction Workflows at ORNL

Oak Ridge National Laboratory (ORNL) experimental neutron science facilities produce 1.2 TB a day of raw event-based data that is stored using the standard metadata-rich NeXus schema built on top of the HDF5 file format. Performance of several data reduction workflows is largely determined by the amount of time spent on the loading and processing algorithms in Mantid, an open-source data analysis framework used across several neutron sciences facilities around the world. The present work introduces new data management algorithms to address identified input output (I/O) bottlenecks on Mantid. First, we introduce an in-memory binary-tree metadata index that resemble NeXus data access patterns to provide a scalable search and extraction mechanism. Second, data encapsulation in Mantid algorithms is optimally redesigned to reduce the total compute and memory runtime footprint associated with metadata I/O reconstruction tasks. Results from this work show speed ups in wall-clock time on ORNL data reduction workflows, ranging from 11% to 30% depending on the complexity of the targeted instrument-specific data. Nevertheless, we highlight the need for more research to address reduction challenges as experimental data volumes increase.

Godoy, William↗

NASA NTRS1994

Dynamic file-access characteristics of a production parallel scientific workload

Multiprocessors have permitted astounding increases in computational performance, but many cannot meet the intense I/O requirements of some scientific applications. An important component of any solution to this I/O bottleneck is a parallel file system that can provide high-bandwidth access to tremendous amounts of data in parallel to hundreds or thousands of processors. Most successful systems are based on a solid understanding of the expected workload, but thus far there have been no comprehensive workload characterizations of multiprocessor file systems. This paper presents the results of a three week tracing study in which all file-related activity on a massively parallel computer was recorded. Our instrumentation differs from previous efforts in that it collects information about every I/O request and about the mix of jobs running in a production environment. We also present the results of a trace-driven caching simulation and recommendations for designers of multiprocessor file systems.

Kotz, David↗

NASA NTRS2013

A Lightweight I/O Scheme to Facilitate Spatial and Temporal Queries of Scientific Data Analytics

In the era of petascale computing, more scientific applications are being deployed on leadership scale computing platforms to enhance the scientific productivity. Many I/O techniques have been designed to address the growing I/O bottleneck on large-scale systems by handling massive scientific data in a holistic manner. While such techniques have been leveraged in a wide range of applications, they have not been shown as adequate for many mission critical applications, particularly in data post-processing stage. One of the examples is that some scientific applications generate datasets composed of a vast amount of small data elements that are organized along many spatial and temporal dimensions but require sophisticated data analytics on one or more dimensions. Including such dimensional knowledge into data organization can be beneficial to the efficiency of data post-processing, which is often missing from exiting I/O techniques. In this study, we propose a novel I/O scheme named STAR (Spatial and Temporal AggRegation) to enable high performance data queries for scientific analytics. STAR is able to dive into the massive data, identify the spatial and temporal relationships among data variables, and accordingly organize them into an optimized multi-dimensional data structure before storing to the storage. This technique not only facilitates the common access patterns of data analytics, but also further reduces the application turnaround time. In particular, STAR is able to enable efficient data queries along the time dimension, a practice common in scientific analytics but not yet supported by existing I/O techniques. In our case study with a critical climate modeling application GEOS-5, the experimental results on Jaguar supercomputer demonstrate an improvement up to 73 times for the read performance compared to the original I/O method.

Temporal Queries↗

DOE OSTI2021

Efficient loading of reduced data ensembles produced at ORNL SNS/HFIR neutron time-of-flight facilities

We present algorithmic improvements to the loading operations of certain reduced data ensembles produced from neutron scattering experiments at Oak Ridge National Laboratory (ORNL) facilities. Ensembles from multiple measurements are required to cover a wide range of the phase space of a sample material of interest. They are stored using the standard NeXus schema on individual HDF5 files. This makes it a scalability challenge, as the number of experiments stored increases in a single ensemble file. The present work follows up on our previous efforts on data management algorithms, to address identified input output (I/O) bottlenecks in Mantid, an open-source data analysis framework used across several neutron science facilities around the world. We reuse an in-memory binary-tree metadata index that resembles data access patterns, to provide a scalable search and extraction mechanism. In addition, several memory operations are refactored and optimized for the current common use cases, ranging most frequently from 10 to 180, and up to 360 separate measurement configurations. Results from this work show consistent speed ups in wall-clock time on the Mantid LoadMD routine, ranging from 19% to 23% on average, on ORNL production computing systems. The latter depends on the complexity of the targeted instrument-specific data and the system I/O and compute variability for the shared computational resources available to users of ORNL’s Spallation Neutron Source (SNS) and the High Flux Isotope Reactor (HFIR) instruments. Nevertheless, we continue to highlight the need for more research to address reduction challenges as experimental data volumes, user time and processing costs increase.

Godoy, William↗

DOE OSTI2019

Measuring the impact of burst buffers on data-intensive scientific workflows

Science applications frequently produce and consume large volumes of data, but delivering this data to and from compute resources can be concerning, as parallel file system performance is not keeping up with compute and memory performance. To alter this I/O bottleneck, some systems have deployed burst buffers, but their impact on performance for real-world scientific workflow applications is still not clear. In this paper, we examine the impact of burst buffers through the remote-shared, allocatable burst buffers on the Cori system at NERSC. By running two data-intensive workflows, a high-throughput genome analysis workflow, and a subset of the SCEC high-performance CyberShake workflow, a production seismic hazard analysis workflow, we find that using burst buffers offers read and write improvements of an order of magnitude, and these improvements lead to increased job performance, and thereby increased overall workflow performance, even for long-running CPU-bound jobs.

42 ENGINEERING↗

DOE OSTI2023

In-pixel AI for lossy data compression at source for X-ray detectors

Integrating neural networks for data compression directly in the Read-Out Integrated Circuits (ROICs), i.e. the pixelated front-end, would result in a significant reduction in off-chip data transfer, overcoming the I/O bottleneck. Our ROIC test chip (AI-In-Pixel-65) is designed in a 65 nm Low Power CMOS process for the readout of pixelated X-ray detectors. Each pixel consists of an analog front-end for signal processing and a 10b analog-to-digital converter operating at 100KSPS. Here, we compare two non-reconfigurable techniques, Principal Component Analysis (PCA) and an AutoEncoder (AE) as lossy data compression engines implemented within the pixelated area. The PCA algorithm achieves 50$×$ compression, adds one clock cycle latency, and results in a 21% increase in the pixel area. The AE achieves 70$×$ compression, adds 30 clock cycle latency, and results in a similar area increase.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Performance Optimization for Relative-Error-Bounded Lossy Compression on Scientific Data

Scientific simulations in high-performance computing (HPC) environments generate vast volume of data, which may cause a severe I/O bottleneck at runtime and a huge burden on storage space for postanalysis. Unlike traditional data reduction schemes such as deduplication or lossless compression, not only can error-controlled lossy compression significantly reduce the data size but it also holds the promise to satisfy user demand on error control. Pointwise relative error bounds (i.e., compression errors depends on the data values) are widely used by many scientific applications with lossy compression since error control can adapt to the error bound in the dataset automatically. Pointwise relative-error-bounded compression is complicated and time consuming. In this article, we develop efficient precomputation-based mechanisms based on the SZ lossy compression framework. Our mechanisms can avoid costly logarithmic transformation and identify quantization factor values via a fast table lookup, greatly accelerating the relative-error-bounded compression with excellent compression ratios. In addition, we reduce traversing operations for Huffman decoding, significantly accelerating the decompression process in SZ. Experiments with eight well-known real-world scientific simulation datasets show that our solution can improve the compression and decompression rates (i.e., the speed) by about 40 and 80 p, respectively, in most of cases, making our designed lossy compression strategy the best-in-class solution in most cases.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2021

DXT Explorer v0.1

DTX Explorer is a tool to generate interactive data visualizations of Darshan I/O traces collected from HPC applications. Its goal is to provide an easy and interactive way for researchers and developers to explore their application's I/O behavior and detect possible I/O bottlenecks that are impacting performance.

Bez, Jean Luca↗

DOE OSTI2022

DXT Explorer v2.0

Bez, JeanLuca↗