搜索结果：Dragonflies

Saturn's moon Titan is a prime destination for investigating prebiotic chemistry beyond Earth, particularly at impact crater sites where transient liquid water may have enabled aqueous reactions between organic molecules. Selk crater represents one such environment and is a primary target of NASA's Dragonfly mission. Here, we present a thermodynamic assessment of nucleobases, ribose, and fatty acids formed from simple atmospheric precursors (HCN and C2H2) within a Selk-sized aqueous melt pool across varying ammonia (NH3) abundances. We find that ammonia acts as a chemical gatekeeper for molecular accessibility. In NH3-free systems, accessibility is restricted to adenine and butanoic acid. Once >=1% NH3 is introduced, all investigated molecular classes become thermodynamically accessible. Distinct molecular classes have different NH3 sensitivities: nucleobases, ribose, and C2-C6 fatty acids yield peaks at 1% NH3, and C7-C12 fatty acids yield peaks at 2% NH3. The modeled preference for pyrimidines vs. purines and monotonic decline of fatty acid abundance with chain length qualitatively mirror patterns observed in carbonaceous meteorites and returned asteroid samples. We show how m

The rise of distributed applications and cloud computing has created a demand for scalable, high-performance key-value storage systems. This paper presents a performance evaluation of three prominent NoSQL key-value stores: Redis, Aerospike, and Dragonfly, using the Yahoo! Cloud Serving Benchmark (YCSB) framework. We conducted extensive experiments across three distinct workload patterns (read-heavy, write-heavy), and balanced while systematically varying client concurrency from 1 to 32 clients. Our evaluation methodology captures both latency, throughput, and memory characteristics under realistic operational conditions, providing insights into the performance trade-offs and scalability behaviour of each system

We investigate the dynamics and the stability of the incompressible flow past a corrugated dragonfly-inspired airfoil in the two-dimensional (2D) $α-Re$ parameter space, where $α$ is the angle of attack and $Re$ is the Reynolds number. The angle of attack is varied between $-5^\circ \le α\le 10^\circ$, and $Re$ (based on the free-stream velocity and the airfoil chord) is increased up to $Re=6000$. The study relies on linear stability analyses and three-dimensional (3D) nonlinear direct numerical simulations. For all $α$ the primary instability consists of a Hopf bifurcation towards a periodic regime. The linear stability analysis reveals that two distinct modes drive the flow bifurcation for positive and negative $α$, being characterised by a different frequency and a distinct triggering mechanism. The critical $Re$ decreases as $|α|$ increases, and scales as a power law for large positive/negative $α$. At intermediate $Re$, different limit cycles arise depending on $α$, each one characterised by a distinctive vortex interaction, leading thus to secondary instabilities of different nature. For intermediate positive/negative $α$ vortices are shed from both the top/bottom leading- an

Dragonfly is a deep reinforcement learning library focused on modularity, in order to ease experimentation and developments. It relies on a json serialization that allows to swap building blocks and perform parameter sweep, while minimizing code maintenance. Some of its features are specifically designed for CPU-intensive environments, such as numerical simulations. Its performance on standard agents using common benchmarks compares favorably with the literature.

The accurate localization and tracking of dynamic targets, such as equipment, people, vehicles, drones, robots, and the assets that they interact with in GPS-denied indoor environments is critical to enabling safe and efficient operations in the next generation of spatially aware industrial facilities. This paper presents DragonFly , a 3D localization system of highly dynamic backscatter tags using a single MIMO mmWave radar. The system delivers the first demonstration of a mmWave backscatter system capable of exploiting the capabilities of MIMO radars for the 3D localization of mmID tags moving at high speeds and accelerations at long ranges by introducing a critical Doppler disambiguation algorithm and a fully integrated cross-polarized dielectric lens-based mmID tag consuming a mere 68 uW. DragonFly was extensively evaluated in static and dynamic configurations, including on a flying quadcopter, and benchmarked against multiple baselines, demonstrating its ability to track the positions of multiple tags with a median 3D accuracy of 12 cm at speeds and acceleration on the order of 10 m/s and 4 m/s^2 and at ranges of up to 50m.

The Dragonfly Spectral Line Mapper is a mosaic telescope comprising 120 Canon telephoto lenses, based on the design of the Dragonfly Telephoto Array. With a wide field of view, and the addition of the "Dragonfly Filter-Tilter" instrumentation holding ultra narrow bandpass filters in front of each lens, the Dragonfly Spectral Line mapper is optimized for ultra low surface brightness imaging of visible wavelength line emission. The Dragonfly Spectral Line Mapper was constructed and commissioned in four phases from March 2022 to November 2023. During this time, four individual mounts of 30 lenses each were constructed and commissioned. The commissioning of the telescope included the deployment of the "Dragonfly StarChaser" which carries out image stabilization corrections in the telephoto lens, to enable hour-long exposures to be taken. In addition, we introduced new instrumentation such as a film to cover the optics to keep the filters clean. Here we describe the updated design of the complete 120-lens array, and the implementation of the instrumentation described above. Additionally, we present updated characterization of the cameras and filter transmission for the full array. Final

The Dragonfly network, with its high-radix and low-diameter structure, is a leading interconnect in high-performance computing. A major challenge is workload interference on shared network links. Parallel discrete event simulation (PDES) is commonly used to analyze workload interference. However, high-fidelity PDES is computationally expensive, making it impractical for large-scale or real-time scenarios. Hybrid simulation that incorporates data-driven surrogate models offers a promising alternative, especially for forecasting application runtime, a task complicated by the dynamic behavior of network traffic. We present \ourmodel, a surrogate model that combines graph neural networks (GNNs) and large language models (LLMs) to capture both spatial and temporal patterns from port level router data. \ourmodel outperforms existing statistical and machine learning baselines, enabling accurate runtime prediction and supporting efficient hybrid simulation of Dragonfly networks.

Dragonfly class of networks are considered as promising interconnects for next-generation supercomputers. While Dragonfly+ networks offer more path diversity than the original Dragonfly design, they are still prone to performance variability due to their hierarchical architecture and resource sharing design. Event-driven network simulators are indispensable tools for navigating complex system design. In this study, we quantitatively evaluate a variety of application communication interactions on a 3,456-node Dragonfly+ system by using the CODES toolkit. This study looks at the impact of communication interference from a user's perspective. Specifically, for a given application submitted by a user, we examine how this application will behave with the existing workload running in the system under different job placement policies. Our simulation study considers hundreds of experiment configurations including four target applications with representative communication patterns under a variety of network traffic conditions. Our study shows that intra-job interference can cause severe performance degradation for communication-intensive applications. Inter-job interference can generally be

Existing high-performance computing (HPC) interconnection architectures are based on high-radix switches, which limits the injection/local performance and introduces latency/energy/cost overhead. The new wafer-scale packaging and high-speed wireline technologies provide high-density, low-latency, and high-bandwidth connectivity, thus promising to support direct-connected high-radix interconnection architecture. In this paper, we propose a wafer-based interconnection architecture called Switch-Less-Dragonfly-on-Wafers. By utilizing distributed high-bandwidth networks-on-chip-on-wafer, costly high-radix switches of the Dragonfly topology are eliminated while increasing the injection/local throughput and maintaining the global throughput. Based on the proposed architecture, we also introduce baseline and improved deadlock-free minimal/non-minimal routing algorithms with only one additional virtual channel. Extensive evaluations show that the Switch-Less-Dragonfly-on-Wafers outperforms the traditional switch-based Dragonfly in both cost and performance. Similar approaches can be applied to other switch-based direct topologies, thus promising to power future large-scale supercomputers.

The Dragonfly Telephoto Array employs a unique design to detect very large and diffuse galaxies, which might be missed with conventional telescopes. The Dragonfly Ultrawide Survey (DFUWS) is a new wide-field survey which will cover 10,000 deg$^2$ of the northern sky, and it provides an ideal dataset to find these large diffuse galaxies. From 3100 deg$^2$ of DFUWS data, we identified eleven large, low surface brightness galaxies as a pilot sample for spectroscopic follow-up. These are the largest galaxies in the examined area that appear smooth and isolated, with effective radii of 12"-27". Eight are below 24 $\mathrm{mag\,arcsec^{-2}}$ in central $g$-band surface brightness. Keck Cosmic Web Imager (KCWI) spectra of the diffuse light show that all eleven galaxies in this sample are quiescent, and seven qualify as ultra-diffuse galaxies (UDGs). Eight galaxies have distances between 15 and 30 Mpc, while the other three are in the Pegasus cluster at 50 Mpc. Their spectra show evidence of a $\sim 1$Gyr old stellar population in addition to an even older stellar population. The intermediate-age component is present in group and satellite galaxies but not in the Pegasus cluster UDGs. All

Dragonfly interconnect is a crucial network technology for supercomputers. To support exascale systems, network resources are shared such that links and routers are not dedicated to any node pair. While link utilization is increased, workload performance is often offset by network contention. Recently, intelligent routing built on reinforcement learning demonstrates higher network throughput with lower packet latency. However, its effectiveness in reducing workload interference is unknown. In this work, we present extensive network simulations to study multi-workload contention under different routing mechanisms, intelligent routing and adaptive routing, on a large-scale Dragonfly system. We develop an enhanced network simulation toolkit, along with a suite of workloads with distinctive communication patterns. We also present two metrics to characterize application communication intensity. Our analysis focuses on examining how different workloads interfere with each other under different routing mechanisms by inspecting both application-level and network-level metrics. Several key insights are made from the analysis.

This paper introduces the Minimal Biorobotic Stealth Distance (MBSD), a novel quantitative metric to evaluate the bionic resemblance of biorobotic aircraft. Current technological limitations prevent dragonfly-inspired aircrafts from achieving optimal performance at biological scales. To address these challenges, we use the DDD-1 dragonfly-inspired aircraft, a hover-capable direct-drive aircraft, to explore the impact of the MBSD on aircraft design. Key contributions of this research include: (1) the establishment of the MBSD as a quantifiable and operable evaluation metric that influences aircraft design, integrating seamlessly with the overall design process and providing a new dimension for optimizing bionic aircraft, balancing mechanical attributes and bionic characteristics; (2) the creation and analysis of a typical aircraft in four directions: essential characteristics of the MBSD, its coupling relationship with existing performance metrics (Longest Hover Duration and Maximum Instantaneous Forward Flight Speed), multi-objective optimization, and application in a typical mission scenario; (3) the construction and validation of a full-system model for the direct-drive dragonfly

High-radix interconnects such as Dragonfly and its variants rely on adaptive routing to balance network traffic for optimum performance. Ideally, adaptive routing attempts to forward packets between minimal and non-minimal paths with the least congestion. In practice, current adaptive routing algorithms estimate routing path congestion based on local information such as output queue occupancy. Using local information to estimate global path congestion is inevitably inaccurate because a router has no precise knowledge of link states a few hops away. This inaccuracy could lead to interconnect congestion. In this study, we present Q-adaptive routing, a multi-agent reinforcement learning routing scheme for Dragonfly systems. Q-adaptive routing enables routers to learn to route autonomously by leveraging advanced reinforcement learning technology. The proposed Q-adaptive routing is highly scalable thanks to its fully distributed nature without using any shared information between routers. Furthermore, a new two-level Q-table is designed for Q-adaptive to make it computational lightly and saves 50% of router memory usage compared with the previous Q-routing. We implement the proposed Q-a