Modern cache hierarchies are tangled webs of complexity. Multiple tiers of heterogeneous physical and virtual devices, with many configurable parameters, all contend to optimally serve swarms of requests between local and remote applications. The challenge of effectively designing these systems is exacerbated by continuous advances in hardware, firmware, innovation in cache eviction algorithms, and evolving workloads and access patterns. This rapidly expanding configuration space has made it costly and time-consuming to physically experiment with numerous cache configurations for even a single stable workload. Current cache evaluation techniques (e.g., Miss Ratio Curves) are short-sighted: they analyze only a single tier of cache, focus primarily on performance, and fail to examine the critical relationships between metrics like throughput and monetary cost. Publicly available I/O cache simulators are also lacking: they can only simulate a fixed or limited number of cache tiers, are missing key features, or offer limited analyses.
It is our position that best practices in cache analysis should include the evaluation of multi-tier configurations, coupled with more comprehensive metrics that reveal critical design trade-offs, especially monetary costs. We are developing an n-level I/O cache simulator that is general enough to model any cache hierarchy, captures many metrics, provides a robust set of analysis features, and is easily extendable to facilitate experimental research or production level provisioning. To demonstrate the value of our proposed metrics and simulator, we extended an existing cache simulator (PyMimircache). We present several interesting and counter-intuitive results in this paper.
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Performance metrics to unleash the power of self-driving labs in chemistry and materials science
With the rise of self-driving labs (SDLs) and automated experimentation across chemical and materials sciences, there is a considerable challenge in designing the best autonomous lab for a given problem based on published studies alone. Determining what digital and physical features are germane to a specific study is a critical aspect of SDL design that needs to be approached quantitatively. Even when controlling for features such as dimensionality, every experimental space has unique requirements and challenges that influence the design of the optimal physical platform and algorithm. Metrics such as optimization rate are therefore not necessarily indicative of the capabilities of an SDL across different studies. In this perspective, we highlight some of the critical metrics for quantifying performance in SDLs to better guide researchers in implementing the most suitable strategies. We then provide a brief review of the existing literature under the lens of quantified performance as well as heuristic recommendations for platform and experimental space pairings.
more » « less- NSF-PAR ID:
- 10490860
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 15
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Modern cache hierarchies are tangled webs of complexity. Multiple tiers of heterogeneous physical and virtual devices, with many configurable parameters, all contend to optimally serve swarms of requests between local and remote applications. The challenge of effectively designing these systems is exacerbated by continuous advances in hardware, firmware, innovation in cache eviction algorithms, and evolving workloads and access patterns. This rapidly expanding configuration space has made it costly and time-consuming to physically experiment with numerous cache configurations for even a single stable workload. Current cache evaluation techniques (e.g., Miss Ratio Curves) are short-sighted: they analyze only a single tier of cache, focus primarily on performance, and fail to examine the critical relationships between metrics like throughput and monetary cost. Publicly available I/O cache simulators are also lacking: they can only simulate a fixed or limited number of cache tiers, are missing key features, or offer limited analyses. It is our position that best practices in cache analysis should include the evaluation of multi-tier configurations, coupled with more comprehensive metrics that reveal critical design trade-offs, especially monetary costs. We are developing an n-level I/O cache simulator that is general enough to model any cache hierarchy, captures many metrics, provides a robust set of analysis features, and is easily extendable to facilitate experimental research or production level provisioning. To demonstrate the value of our proposed metrics and simulator, we extended an existing cache simulator (PyMimircache). We present several interesting and counter-intuitive results in this paper.more » « less
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Abstract Aim Rapid global change is impacting the diversity of tree species and essential ecosystem functions and services of forests. It is therefore critical to understand and predict how the diversity of tree species is spatially distributed within and among forest biomes. Satellite remote sensing platforms have been used for decades to map forest structure and function but are limited in their capacity to monitor change by their relatively coarse spatial resolution and the complexity of scales at which different dimensions of biodiversity are observed in the field. Recently, airborne remote sensing platforms making use of passive high spectral resolution (i.e., hyperspectral) and active lidar data have been operationalized, providing an opportunity to disentangle how biodiversity patterns vary across space and time from field observations to larger scales. Most studies to date have focused on single sites and/or one sensor type; here we ask how multiple sensor types from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) perform across multiple sites in a single biome at the NEON field plot scale (i.e., 40 m × 40 m).
Location Eastern USA.
Time period 2017–2018.
Taxa studied Trees.
Methods With a fusion of hyperspectral and lidar data from the NEON AOP, we assess the ability of high resolution remotely sensed metrics to measure biodiversity variation across eastern US temperate forests. We examine how taxonomic, functional, and phylogenetic measures of alpha diversity vary spatially and assess to what degree remotely sensed metrics correlate with in situ biodiversity metrics.
Results Models using estimates of forest function, canopy structure, and topographic diversity performed better than models containing each category alone. Our results show that canopy structural diversity, and not just spectral reflectance, is critical to predicting biodiversity.
Main conclusions We found that an approach that jointly leverages spectral properties related to leaf and canopy functional traits and forest health, lidar derived estimates of forest structure, fine‐resolution topographic diversity, and careful consideration of biogeographical differences within and among biomes is needed to accurately map biodiversity variation from above.
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Magnetically-actuated swimming microrobots are an emerging tool for navigating and manipulating materials in confined spaces. Recent work has demonstrated that it is possible to build such systems at the micro and nanoscales using polymer microspheres, magnetic particles and DNA nanotechnology. However, while these materials enable an unprecedented ability to build at small scales, such systems often demonstrate significant polydispersity resulting from both the material variations and the assembly process itself. This variability makes it difficult to predict, let alone optimize, the direction or magnitude of microswimmer velocity from design parameters such as link shape or aspect ratio. To isolate questions of a swimmer's design from variations in its physical dimensions, we present a novel experimental platform using two-photon polymerization to build a two-link, buoyant milliswimmer with a fully customizable shape and integrated flexible linker (the swimmer is underactuated, enabling asymmetric cyclic motion and net translation). Our approach enables us to control both swimming direction and repeatability of swimmer performance. These studies provide ground truth data revealing that neither the first order nor second order models currently capture the key features of milliswimmer performance. We therefore use our experimental platform to develop design guidelines for tuning the swimming speeds, and we identify the following three approaches for increasing speed: (1) tuning the actuation frequency for a fixed aspect ratio, (2) adjusting the aspect ratio given a desired range of operating frequencies, and (3) using the weaker value of linker stiffness from among the values that we tested, while still maintaining a robust connection between the links. We also find experimentally that spherical two-link swimmers with dissimilar link diameters achieve net velocities comparable to swimmers with cylindrical links, but that two-link spherical swimmers of equal diameter do not.more » « less
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Abstract The incorporation of high‐performance optoelectronic devices into photonic neuromorphic processors can substantially accelerate computationally intensive matrix multiplication operations in machine learning (ML) algorithms. However, the conventional designs of individual devices and system are largely disconnected, and the system optimization is limited to the manual exploration of a small design space. Here, a device‐system end‐to‐end design methodology is reported to optimize a free‐space optical general matrix multiplication (GEMM) hardware accelerator by engineering a spatially reconfigurable array made from chalcogenide phase change materials. With a highly parallelized integrated hardware emulator with experimental information, the design of unit device to directly optimize GEMM calculation accuracy is achieved by exploring a large parameter space through reinforcement learning algorithms, including deep Q‐learning neural network, Bayesian optimization, and their cascaded approach. The algorithm‐generated physical quantities show a clear correlation between system performance metrics and device specifications. Furthermore, physics‐aware training approaches are employed to deploy optimized hardware to the tasks of image classification, materials discovery, and a closed‐loop design of optical ML accelerators. The demonstrated framework offers insights into the end‐to‐end and co‐design of optoelectronic devices and systems with reduced human supervision and domain knowledge barriers.
