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At least 883 records · Page 49

Platform Agnostic Streaming Data Application Performance Models

The mapping of computational needs onto execution resources is, by and large, a manual task, and users are frequently guided simply by intuition and past experiences. We present a queueing theory based performance model for streaming data applications that takes steps towards a better understanding of resource mapping decisions, thereby assisting application developers to make good mapping choices. The performance model (and associated cost model) are agnostic to the specific properties of the compute resource and application, simply characterizing them by their achievable data throughput. We illustrate the model with a pair of applications, one chosen from the field of computational biology and the second is a classic machine learning problem.

Faber, Clayton↗

HAPPA: A Modular Platform for HPC Application Resilience Analysis with LLMs Embedded

High-performance computing (HPC) systems are increasingly vulnerable to soft errors, which pose significant challenges in maintaining computational accuracy and reliability. Predicting the resilience of HPC applications to these errors is crucial for robust code protection and detailed resilience analysis. In this study, we present HAppA, a modular platform designed for HPC Application Resilience Analysis. Embedding Large Language Models (LLMs), HAppA addresses understanding the context information of long code sequences typical in HPC applications. HAppA implements a novel code representation module that chunks the code into fixed-size segments and aggregates the embeddings of these segments. Three aggregation methods have been explored: MeanPooling, MaxPooling, and LSTM-based techniques. We built a DAtaset for REsilience analysis using Fault Injection (FI), named DARE. Using our DARE dataset, HAppA is trained for regression prediction tasks. Our evaluation results demonstrate the predictive accuracy of HAppA compared to other models, particularly noting that the LSTM-based aggregation method -- HAppA-LSTM -- achieves a mean squared error (MSE) of 0.078 for SDC prediction, surpassing the existing state-of-the-art PARIS model, which recorded an MSE of 0.1172. Additionally, HAppA with the KeyBERT model extracts a list of keywords representing the source code. A comprehensive importance analysis of these keywords further elucidates the code patterns contributing to the error rate. These findings highlight the effectiveness of HAppA in analyzing the resilience of HPC applications and establish a new benchmark for predictive accuracy in resilience.

Jiang, Hailong [Kent State University]↗

Circular Corrugated Miter Bend and Gap Losses for Broadband Frequency Applications

Circular corrugated waveguides are often used in fusion applications at single, multiple, or broadband millimeter frequencies due to their low ohmic loss, expected large frequency bandwidth, and direct coupling to free-space Gaussian modes. For single-frequency corrugated waveguide systems, transmission line components can be optimized to the desired frequency. For broadband or multiple-frequency applications, this is not possible. The goal of this paper is to demonstrate that the frequency bandwidth of circular corrugated waveguides can be compromised by diffraction losses to miter bends and gaps. It is shown that if the corrugation depth differs significantly from λ /4, a theory can substantially underpredict the gap and miter bend losses. The simulations are also shown to compare favorably to experimental measurements. To improve the transmission line performance in large frequency bandwidth systems, such as 33–165 GHz reflectometry, reducing the number of miter bends may, therefore, be necessary. For the improvement of performance in narrower frequency bandwidth or multiple-frequency systems, especially high-powered systems, wavelength-dependent techniques may be applicable. Furthermore, one such application is shown for a Bragg reflection technique to reduce the thermal load to insulating ceramic rings in a dc break component for multiple-frequency ITER electron cyclotron heating transmission lines.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Cadmium Magnesium Telluride for Next-Generation X-Ray Free Electron Laser, Synchrotron, and Many Other Applications

We developed a picosecond photodetector based on our Bridgman-grown and specially engineered cadmium magnesium telluride (Cd 1–x MgxTe) single crystal that is sensitive to both optical and X-ray pulses for coarse timing in free-electron laser applications. Cd 1–x MgxTe is a widebandgap semiconductor with potential applications, not only in optoelectronics, but also in particle physics as an intense pulse radiation detector for bremsstrahlung, X-ray/gamma-ray radiation, thermal neutrons, and medical imaging. For femtosecond optical and X-ray crosscorrelation, the material must have a very short lifetime, a condition that is opposite to that required for nuclear spectroscopy applications. At the same time, the material also needs to have a very low bulk leakage current, in the 10–90 nA range for voltages to even 1000 V. Hence, the ability to tailor or engineer the material is very crucial. Picosecond response and the crystal growth of this specially engineered Cd 1–x MgxTe material are presented. Furthermore, other characterization and transient measurements are discussed along with room-temperature semiconductor detector performance for other nuclear radiation detection applications.

36 MATERIALS SCIENCE↗

Dynamic Adaptable Asynchronous Progress Model for MPI RMA Multiphase Applications

Casper is a process-based asynchronous progress model for MPI one-sided communication on multi-and many-core architectures. The one-sided communication is not truly one-sided in most MPI implementations: the target process still relies on software progress to complete incoming operations. Casper allows the user to specify an arbitrary number of cores dedicated to background ghost processes and transparently redirects the RMA operations to ghost processes by utilizing the PMPI redirection and MPI-3 shared-memory technologies. Although Casper benefits applications that suffer from lack of asynchronous progress, the operation redirection design might not support complex multiphase applications effectively, which often involve dynamically changing communication density and computing workloads. In this paper, we present an adaptive mechanism in Casper to address the limitation of static asynchronous progress in multiphase applications. For this we exploit two adaptive strategies, a user-guided strategy and a fully transparent and automatic strategy based on self-profiling and prediction, to dynamically reconfigure the asynchronous progress in Casper according to real-time performance characteristics during multiphase execution. We evaluate the adaptive approaches in both microbenchmarks and a real quantum chemistry application suite, NWChem, on the Cray XC30 supercomputer and an Intel Omni-Path cluster.

97 MATHEMATICS AND COMPUTING↗

Improving I/O Performance for Exascale Applications through Online Data Layout Reorganization

The applications being developed within the U.S. Exascale Computing Project (ECP) to run on imminent Exascale computers will generate scientific results with unprecedented fidelity and record turn-around time. Many of these codes are based on particle-mesh methods and use advanced algorithms, especially dynamic load-balancing and mesh-refinement, to achieve high performance on Exascale machines. Yet, as such algorithms improve parallel application efficiency, they raise new challenges for I/O logic due to their irregular and dynamic data distributions. Thus, while the enormous data rates of Exascale simulations already challenge existing file system write strategies, the need for efficient read and processing of generated data introduces additional constraints on the data layout strategies that can be used when writing data to secondary storage. We review these I/O challenges and introduce two online data layout reorganization approaches for achieving good tradeoffs between read and write performance. We demonstrate the benefits of using these two approaches for the ECP particle-in-cell simulation WarpX, which serves as a motif for a large class of important Exascale applications. Here, we show that by understanding application I/O patterns and carefully designing data layouts we can increase read performance by more than 80 percent.

97 MATHEMATICS AND COMPUTING↗

Object Proxy Patterns for Accelerating Distributed Applications

Workflow and serverless frameworks have empowered new approaches to distributed application design by abstracting compute resources. However, their typically limited or one-size-fits-all support for advanced data flow patterns leaves optimization to the application programmer—optimization that becomes more difficult as data become larger. The transparent object proxy, which provides wide-area references that can resolve to data regardless of location, has been demonstrated as an effective low-level building block in such situations. Here we propose three high-level proxy-based programming patterns—distributed futures, streaming, and ownership—that make the power of the proxy pattern usable for more complex and dynamic distributed program structures. We motivate these patterns via careful review of application requirements and describe implementations of each pattern. As a result, we evaluate our implementations through a suite of benchmarks and by applying them in three meaningful scientific applications, in which we demonstrate substantial improvements in runtime, throughput, and memory usage.

Distributed Computing↗

Numerical Analysis of 2-D and 3-D MHD Flows Relevant to Fusion Applications

Here, the analysis of many fusion applications such as liquid-metal blankets requires application of computational fluid dynamics (CFD) methods for electrically conductive liquids in geometrically complex regions and in the presence of a strong magnetic field. A current state of the art general purpose CFD code allows modeling of the flow in complex geometric regions, with simultaneous conjugated heat transfer analysis in liquid and surrounding solid parts. Together with a magnetohydrodynamics (MHD) capability, the general purpose CFD code will be a valuable tool for the design and optimization of fusion devices. This paper describes an introduction of MHD capability into the general purpose CFD code CFX, part of the ANSYS Workbench. The code was adapted for MHD problems using a magnetic induction approach. CFX allows introduction of user-defined variables using transport or Poisson equations. For MHD adaptation of the code three additional transport equations were introduced for the components of the magnetic field, in addition to the Poisson equation for electric potential. The Lorentz force is included in the momentum transport equation as a source term. Fusion applications usually involve very strong magnetic fields, with values of the Hartmann number of up to tens of thousands. In this situation a system of MHD equations become very rigid with very large source terms and very strong variable gradients. To increase system robustness, special measures were introduced during the iterative convergence process, such as linearization using source coefficient for momentum equations. The MHD implementation in general purpose CFD code was tested against benchmarks, specifically selected for liquid-metal blanket applications. Results of numerical simulations using present implementation closely match analytical solutions for a Hartmann number of up to 1500 for a 2-D laminar flow in the duct of square cross section, with conducting and nonconducting walls. Results for a 3-D test case are also included.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Biochar application as a tool to decrease soil nitrogen losses (NH3 volatilization, N2O emissions, and N leaching) from croplands: Options and mitigation strength in a global perspective

Biochar application to croplands has been proposed as a potential strategy to decrease losses of soil-reactive nitrogen (N) to the air and water. However, the extent and spatial variability of biochar function at the global level are still unclear. Using Random Forest regression modelling of machine learning based on data compiled from the literature, we mapped the impacts of different biochar types (derived from wood, straw, or manure), and their interactions with biochar application rates, soil properties, and environmental factors, on soil N losses (NH3 volatilization, N2O emissions, and N leaching) and crop productivity. The results show that a suitable distribution of biochar across global croplands (i.e., one application of <40 t ha-1 wood biochar for poorly buffered soils, such as those characterized by soil pH<5, organic carbon<1%, or clay>30%; and one application of <80 t ha-1 wood biochar, <40 t ha-1 straw biochar, or <10 t ha-1 manure biochar for other soils) could achieve an increase in global crop yields by 222–766 Tg yr-1 (4%–16% increase), a mitigation of cropland N2O emissions by 0.19–0.88 Tg N yr-1 (6%–30% decrease), a decline of cropland N leaching by 3.9–9.2 Tg N yr-1 (12%–29% decrease), but also a fluctuation of cropland NH3 volatilization by -1.9–4.7 Tg N yr-1 (-12%–31% change). The decreased sum of the three major reactive N losses amount to 1.7–9.4 Tg N yr-1, which corresponds to 3%–14% of the global cropland total N loss. Biochar generally has a larger potential for decreasing soil N losses but with less benefits to crop production in temperate regions than in tropical regions.

biochar, soil amendment, nitrogen cycle, climate c↗

IoT Devices and Applications for Wire-Based Hybrid Manufacturing Machine Tools

Hybrid manufacturing machine tools have the potential to be a disruptive technology as they can leverage the benefits of both additive and subtractive manufacturing by incorporating both processes on the same machine while limiting the downsides of the individual processes. Since these machines use two very disparate manufacturing processes and hybrid manufacturing is an emerging technology, it will be useful to monitor data coming from the machine and apply it to improve the manufacturing process, the operation of the machine, and to integrate the machine into the larger digital framework of Industrial Internet of Things (IoT). The present work discusses IoT devices that would be beneficial to add to a hybrid machine tool as well as applications for those devices. The proposed methods discussed in this work have not been experimentally implemented on a hybrid machine tool and so there are no performance data available yet. The hybrid machine tool used as a basis to generate these IoT applications is the Mazak VC-500A/5x AM Hot Wire Deposition, which is a 5-axis machine tool incorporated with a wire feedstock 4kW laser deposition system. Methodologies and applications will be outlined for machine health and process monitoring. Other areas covered include process benchmarking, secure networking options for the proposed IoT framework, and hybrid process improvement. Limitations of these methods and future work for new sensor devices and application areas is also discussed.

Thien, Austen↗

Application Crash Consistency and Performance with CCFS

Recent research has shown that applications often incorrectly implement crash consistency. In this paper, we present the Crash-Consistent File System (ccfs), a file system that improves the correctness of application-level crash consistency protocols while maintaining high performance. A key idea in ccfs is the abstraction of a stream. Within a stream, updates are committed in program order, improving correctness; across streams, there are no ordering restrictions, enabling scheduling flexibility and high performance. We empirically demonstrate that applications running atop ccfs achieve high levels of crash consistency. Further, we show that ccfs performance under standard file-system benchmarks is excellent, in the worst case on par with the highest performing modes of Linux ext4, and in some cases notably better. Finally, overall, we demonstrate that both application correctness and high performance can be realized in a modern file system.

97 MATHEMATICS AND COMPUTING↗

MPI Stages: Checkpointing MPI State for Bulk Synchronous Applications

When an MPI program experiences a failure, the most common recovery approach is to restart all processes from a previous checkpoint and to re-queue the entire job. A disadvantage of this method is that, although the failure occurred within the main application loop, live processes must start again from the beginning of the program, along with new replacement processes---this incurs unnecessary overhead for live processes. To avoid such overheads and concomitant delays, we introduce the concept of "MPI Stages." MPI Stages saves internal MPI state in a separate checkpoint in conjunction with application state. Upon failure, both MPI and application state are recovered, respectively, from their last synchronous checkpoints and continue without restarting the overall MPI job. Live processes roll back only a few iterations within the main loop instead of rolling back to the beginning of the program, while a replacement of failed process restarts and reintegrates, thereby achieving faster failure recovery. This approach integrates well with large-scale, bulk synchronous applications and checkpoint/restart. In this paper, we identify requirements for production MPI implementations to support state checkpointing with MPI Stages, which includes capturing and managing internal MPI state and serializing and deserializing user handles to MPI objects. We evaluate our fault tolerance approach with a proof-of-concept prototype MPI implementation that includes MPI Stages. We demonstrate its functionality and performance using LULESH and microbenchmarks. Our results show that MPI Stages reduces the recovery time by 13× for LULESH in comparison to checkpoint/restart.

Sultana, Nawrin↗

Optimizing I/O Performance of HPC Applications with Autotuning

Parallel Input output is an essential component of modern high-performance computing (HPC). Obtaining good I/O performance for a broad range of applications on diverse HPC platforms is a major challenge, in part, because of complex inter dependencies between I/O middleware and hardware. The parallel file system and I/O middleware layers all offer optimization parameters that can, in theory, result in better I/O performance. Unfortunately, the right combination of parameters is highly dependent on the application, HPC platform, problem size, and concurrency. Scientific application developers do not have the time or expertise to take on the substantial burden of identifying good parameters for each problem configuration. They resort to using system defaults, a choice that frequently results in poor I/O performance. We expect this problem to be compounded on exascale-class machines, which will likely have a deeper software stack with hierarchically arranged hardware resources. We present as a solution to this problem an autotuning system for optimizing I/O performance, I/O performance modeling, I/O tuning, and I/O patterns. We demonstrate the value of this framework across several HPC platforms and applications at scale.

Behzad, Babak↗

Can Applications Recover from fsync Failures?

We analyze how file systems and modern data-intensive applications react to fsync failures. First, we characterize how three Linux file systems (ext4, XFS, Btrfs) behave in the presence of failures. We find commonalities across file systems (pages are always marked clean, certain block writes always lead to unavailability) as well as differences (page content and failure reporting is varied). Next, we study how five widely used applications (PostgreSQL, LMDB, LevelDB, SQLite, Redis) handle fsync failures. Our findings show that although applications use many failure-handling strategies, none are sufficient: fsync failures can cause catastrophic outcomes such as data loss and corruption. Our findings have strong implications for the design of file systems and applications that intend to provide strong durability guarantees.

Computer Science↗

Automating NISQ Application Design with Meta Quantum Circuits with Constraints (MQCC)

Near-term intermediate scale quantum (NISQ) computers are likely to have very restricted hardware resources, where precisely controllable qubits are expensive, error-prone, and scarce. Programmers of such computers must therefore balance trade-offs among a large number of (potentially heterogeneous) factors specific to the targeted application and quantum hardware. To assist them, we propose Meta Quantum Circuits with Constraints (MQCC), a meta-programming framework for quantum programs. Programmers express their application as a succinct collection of normal quantum circuits stitched together by a set of (manually or automatically) added meta-level choice variables, whose values are constrained according to a programmable set of quantitative optimization criteria. MQCC’s compiler generates the appropriate constraints and solves them via an SMT solver, producing an optimized, runnable program. We showcase a few MQCC’s applications for its generality including an automatic generation of efficient error syndrome extraction schemes for fault-tolerant quantum error correction with heterogeneous qubits and an approach to writing approximate quantum Fourier transformation and quantum phase estimation that smoothly trades off accuracy and resource use. We also illustrate that MQCC can easily encode prior one-off NISQ application designs-–multi-programming (MP), crosstalk mitigation (CM)—as well as a combination of their optimization goals (i.e., a combined MP-CM).

97 MATHEMATICS AND COMPUTING↗

Experiences Readying Applications for Exascale

The advent of Exascale computing invites an assessment of existing best practices for developing application readiness on the world's largest supercomputers. This work details observations from the last four years in preparing scientific applications to run on the Oak Ridge Leadership Computing Facility's (OLCF) Frontier system. This paper addresses a range of topics in software including programmability, tuning, and portability considerations that are key to moving applications from existing systems to future installations. A set of representative workloads provides case studies for general system and software testing. We evaluate the use of early access systems for development across several generations of hardware. Finally, we discuss how best practices were identified and disseminated to the community through a wide range of activities including user-guides and trainings. We conclude with recommendations for ensuring application readiness on future leadership computing systems.

exascale↗

High-Throughput Computing: Case Study of Medical Image Processing Applications

HPC is designed for large-scale simulations using monolithic codes of tightly coupled processes highly optimized to deliver decreased time to solution. Medical image processing is not a traditional field of HPC. Similar to AI applications, medical image processing parses large datasets, typically multiple times, to support a variety of studies for classification, diagnosis or monitoring purposes. The convergence of AI, HPC and Big Data encouraged more fields using image processing to transition to HPC. However, not all applications benefit from the same optimizations. In this paper we focus on high throughput medical image processing applications that analyze a huge dataset of small MRI images and that require HPC systems to decrease the time of parsing the entire dataset and not individual MRIs. We show in this research the performance of running SLANT, an image processing application for a whole brain segmentation, on large-scale systems and highlight performance limitations. We present optimizations prioritizing throughput that exhibit a 3.5x speed-up on the Summit Supercomputer that can be used as a baseline for building a high-throughput execution framework for other HPC systems.

Predescu, Maria↗

Fine-Grained Application Energy and Power Measurements on the Frontier Exascale System

The increasing complexity and power/energy demands of heterogeneous exascale systems, such as the Frontier supercomputer, present significant challenges for measuring and optimizing power consumption in applications. Current tools either lack the resolution to capture fine-grained power and energy measurements, fail to validate in-band measurements against out-of-band power sensors, or cannot integrate this information with application performance events in a scalable manner. This paper introduces a novel open-source performance toolkit that integrates extended PAPI components with Score-P plugins to enable in-band, fine-grained power and energy measurements, while also supporting validation using power meter measurements for both CPUs and GPUs. One key contribution is the ability to perform millisecond-level power and energy measurements for AMD MI250X GPUs, mapping them to application performance events within a single trace and measurement system that scales. Our toolkit combines coarse-grained measurements from cray_pm counters with high-resolution metrics from rocm_smi and RAPL, converting GPU instantaneous accumulated energy into power to capture both transient and steady-state power behavior, a capability often missed by out-of-band and monitoring tools. By mapping these metrics to specific application regions, developers can identify energy hotspots, address inefficiencies in GPU kernel execution, and validate in-band measurements against external measurements. We demonstrate the effectiveness of this approach through case studies using benchmarks such as GPU rocblas_sgemm, BLIS c_blas_dgemm, and rocHPL, highlighting the variability of the measurements and the impact of transient power spikes on kernel-level efficiency.

Hernandez Mendoza, Oscar [ORNL] (ORCID:00000002538↗