Cosine

2021 ◽  
Vol 15 (1) ◽  
pp. 112-126
Author(s):  
Subarna Chatterjee ◽  
Meena Jagadeesan ◽  
Wilson Qin ◽  
Stratos Idreos
Keyword(s):  
First Principles ◽  
Design Space ◽  
Virtual Machines ◽  
State Of The Art ◽  
High Accuracy ◽  
Hash Tables ◽  
Engine Design ◽  
B Trees ◽  
Target Performance ◽  
Average Cosine

We present a self-designing key-value storage engine, Cosine, which can always take the shape of the close to "perfect" engine architecture given an input workload, a cloud budget, a target performance, and required cloud SLAs. By identifying and formalizing the first principles of storage engine layouts and core key-value algorithms, Cosine constructs a massive design space comprising of sextillion (10 36 ) possible storage engine designs over a diverse space of hardware and cloud pricing policies for three cloud providers - AWS, GCP, and Azure. Cosine spans across diverse designs such as Log-Structured Merge-trees, B-trees, Log-Structured Hash-tables, in-memory accelerators for filters and indexes as well as trillions of hybrid designs that do not appear in the literature or industry but emerge as valid combinations of the above. Cosine includes a unified distribution-aware I/O model and a learned concurrency-aware CPU model that with high accuracy can calculate the performance and cloud cost of any possible design on any workload and virtual machines. Cosine can then search through that space in a matter of seconds to find the best design and materializes the actual code of the resulting storage engine design using a templated Rust implementation. We demonstrate that on average Cosine outperforms state-of-the-art storage engines such as write-optimized RocksDB, read-optimized WiredTiger, and very write-optimized FASTER by 53x, 25x, and 20x, respectively, for diverse workloads, data sizes, and cloud budgets across all YCSB core workloads and many variants.

scholarly journals Charting the design space of query execution using VOILA

2021 ◽  
Vol 14 (6) ◽  
pp. 1067-1079
Author(s):  
Tim Gubner ◽  
Peter Boncz
Keyword(s):  
Design Space ◽  
State Of The Art ◽  
Domain Specific Language ◽  
Query Execution ◽  
Exploration Process ◽  
Specific Language ◽  
Domain Specific ◽  
Engine Design ◽  
Query Engine ◽  
Basic Concepts

Database architecture, while having been studied for four decades now, has delivered only a few designs with well-understood properties. These few are followed by most actual systems. Acquiring more knowledge about the design space is a very time-consuming processes that requires manually crafting prototypes with a low chance of generating material insight. We propose a framework that aims to accelerate this exploration process significantly. Our framework enables synthesizing many different engines from a description in a carefully designed domain-specific language (VOILA). We explain basic concepts and formally define the semantics of VOILA. We demonstrate VOILA's flexibility by presenting translation back-ends that allow the synthesis of state-of-the-art paradigms (data-centric compilation, vectorized execution, AVX-512), mutations and mixes thereof. We show-case VOILA's flexibility by exploring the query engine design space in an automated fashion. We generated thousands of query engines and report our findings. Queries generated by VOILA achieve similar performance as state-of-the-art hand-optimized implementations and are up to 35.5X faster than well-known systems.

First-Principles Evaluation of the Potential of Borophene as a Monolayer Transparent Conductor

2017 ◽  
Author(s):  
Lyudmyla Adamska ◽  
Sridhar Sadasivam ◽  
Jonathan J. Foley ◽  
Pierre Darancet ◽  
Sahar Sharifzadeh
Keyword(s):  
First Principles ◽  
Density Functional ◽  
State Of The Art ◽  
Transparent Electrode ◽  
Visible Range ◽  
Two Dimensional ◽  
Transparent Conductor ◽  
Many Body ◽  
Functional Theory ◽  
Many Body Perturbation Theory

Two-dimensional boron is promising as a tunable monolayer metal for nano-optoelectronics. We study the optoelectronic properties of two likely allotropes of two-dimensional boron using first-principles density functional theory and many-body perturbation theory. We find that both systems are anisotropic metals, with strong energy- and thickness-dependent optical transparency and a weak (<1%) absorbance in the visible range. Additionally, using state-of-the-art methods for the description of the electron-phonon and electron-electron interactions, we show that the electrical conductivity is limited by electron-phonon interactions. Our results indicate that both structures are suitable as a transparent electrode.

scholarly journals The Degree of Oxidation of Graphene Oxide

Nanomaterials ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 560
Author(s):  
Alexandra Carvalho ◽  
Mariana C. F. Costa ◽  
Valeria S. Marangoni ◽  
Pei Rou Ng ◽  
Thi Le Hang Nguyen ◽  
...  
Keyword(s):  
Graphene Oxide ◽  
Ab Initio ◽  
State Of The Art ◽  
High Accuracy ◽  
Precise Determination ◽  
Photoemission Spectroscopy ◽  
Pristine Graphene ◽  
X Ray ◽  
Degree Of Oxidation

We show that the degree of oxidation of graphene oxide (GO) can be obtained by using a combination of state-of-the-art ab initio computational modeling and X-ray photoemission spectroscopy (XPS). We show that the shift of the XPS C1s peak relative to pristine graphene, ΔEC1s, can be described with high accuracy by ΔEC1s=A(cO−cl)2+E0, where c0 is the oxygen concentration, A=52.3 eV, cl=0.122, and E0=1.22 eV. Our results demonstrate a precise determination of the oxygen content of GO samples.

scholarly journals A Machine Vision Approach for Bioreactor Foam Sensing

2021 ◽  
pp. 247263032110088
Author(s):  
Jonas Austerjost ◽  
Robert Söldner ◽  
Christoffer Edlund ◽  
Johan Trygg ◽  
David Pollard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
State Of The Art ◽  
Low Cost ◽  
High Accuracy ◽  
Consumer Electronics ◽  
Learning System ◽  
Automotive Applications ◽  
Fine Grained

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

Interface Electronic And Magnetic Structures Of Layered Fe In Contact With MgO

1991 ◽  
Vol 238 ◽  
Author(s):  
Young Keun Kim ◽  
Michael E. McHenry ◽  
Manuel P. Oliveria ◽  
Mark E. Eberhart
Keyword(s):  
First Principles ◽  
State Of The Art ◽  
Magnetic Moments ◽  
Magnetic Structures ◽  
Consistent Field ◽  
Structure Of Metal ◽  
Metal Ceramic ◽  
Layer Technique ◽  
Self Consistent Field ◽  
Ceramic Interfaces

ABSTRACTA model based on the state-of-the-art, first-principles layer Korringa-Kohn-Rostoker (LKKR) method has proven to be very effective in describing the electronic and magnetic structure of metal/ceramic interfaces. We have performed self-consistent field computations incorporating spin polarization both for Fe/MgO superlattice (bulk technique) and for MgO/Fe/MgO sandwich (layer technique) systems. Muffin-tin potentials were employed for both materials in our computations. Iron layer was embedded in MgO, the host material, to have a [110](100)Fe / [100](100)MgO contact configuration. A large enhancement of magnetic moments has been found at the interface.

Deconstructing Web Services

2012 ◽  
Vol 241-244 ◽  
pp. 3171-3174
Author(s):  
Chang Guang Shi
Keyword(s):  
Web Services ◽  
Virtual Machines ◽  
Voice Over Ip ◽  
Current Status ◽  
B Trees

Many experts would agree that, had it not been for telephony, the construction of B-trees might never have occurred. Given the current status of random theory, information theorists urgently desire the unfortunate unification of virtual machines and voice-over-IP, which embodies the unproven principles of robotics. We show that even though voice-over-IP and e-commerce can collaborate to achieve this goal, courseware and Internet QoS can synchronize to realize this mission.

Comparison of Piston Concept Design Solutions for Composite Cycle Engines Part II: Design Considerations

2021 ◽  
Author(s):  
Dimitrios Chatzianagnostou ◽  
Stephan Staudacher
Keyword(s):  
Gas Turbine ◽  
Design Space ◽  
Piston Compressor ◽  
Design System ◽  
Surface Load ◽  
Preliminary Design ◽  
Concept Design ◽  
Piston Engine ◽  
Engine Design ◽  
Cylinder Piston

Abstract Hecto pressure composite cycle engines with piston engines and piston compressors are potential alternatives to advanced gas turbine engines. The nondimensional groups limiting their design have been introduced and generally discussed in Part I [1]. Further discussion shows, that the ratio of effective power to piston surface characterizes the piston thermal surface load capability. The piston design and the piston cooling technology level limit its range of values. Reynolds number and the required ratio of advective to diffusive material transport limit the stroke-to-bore ratio. Torsional frequency sets a limit to crankshaft length and hence cylinder number. A rule based preliminary design system for composite cycle engines is presented. Its piston engine design part is validated against data of existing piston engines. It is used to explore the design space of piston components. The piston engine design space is limited by mechanical feasibility and the crankshaft overlap resulting in a minimum stroke-to-bore ratio. An empirical limitation on stroke-to-bore ratio is based on existing piston engine designs. It limits the design space further. Piston compressor design does not limit the piston engine design but is strongly linked to it. The preliminary design system is applied to a composite cycle engines of 22MW take-off shaft power, flying a 1000km mission. It features three 12-cylinder piston engines and three 20-cylinder piston compressors. Its specific fuel consumption and mission fuel burn are compared to an intercooled gas turbine with pressure gain combustion of similar technology readiness.

Persistent memory hash indexes

2021 ◽  
Vol 14 (5) ◽  
pp. 785-798
Author(s):  
Daokun Hu ◽  
Zhiwen Chen ◽  
Jianbing Wu ◽  
Jianhua Sun ◽  
Hao Chen
Keyword(s):  
Future Development ◽  
High Performance ◽  
Performance Metrics ◽  
Comprehensive Evaluation ◽  
State Of The Art ◽  
Hash Tables ◽  
Trade Offs ◽  
Depth Analysis ◽  
Persistent Memory ◽  
Memory Modules

Persistent memory (PM) is increasingly being leveraged to build hash-based indexing structures featuring cheap persistence, high performance, and instant recovery, especially with the recent release of Intel Optane DC Persistent Memory Modules. However, most of them are evaluated on DRAM-based emulators with unreal assumptions, or focus on the evaluation of specific metrics with important properties sidestepped. Thus, it is essential to understand how well the proposed hash indexes perform on real PM and how they differentiate from each other if a wider range of performance metrics are considered. To this end, this paper provides a comprehensive evaluation of persistent hash tables. In particular, we focus on the evaluation of six state-of-the-art hash tables including Level hashing, CCEH, Dash, PCLHT, Clevel, and SOFT, with real PM hardware. Our evaluation was conducted using a unified benchmarking framework and representative workloads. Besides characterizing common performance properties, we also explore how hardware configurations (such as PM bandwidth, CPU instructions, and NUMA) affect the performance of PM-based hash tables. With our in-depth analysis, we identify design trade-offs and good paradigms in prior arts, and suggest desirable optimizations and directions for the future development of PM-based hash tables.

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