Search results for "Scalability"
showing 10 items of 221 documents
Framework for complex quantum state generation and coherent control based on on-chip frequency combs
2018
Integrated frequency combs introduce a scalable framework for the generation and manipulation of complex quantum states (including multi-photon and high-dimensional states), using only standard silicon chip and fiber telecommunications components.
Run-time scalable NoC for FPGA based virtualized IPs
2017
The integration of virtualized FPGA-based hardware accelerators in a cloud computing is progressing from time to time. As the FPGA has limited resources, the dynamic partial reconfiguration capability of the FPGA is considered to share resources among different virtualized IPs during runtime. On the other hand, the NoC is a promising solution for communication among virtualized FPGA-based IPs. However, not all the virtualized regions of the FPGA will be active all the time. When there is no demand for virtualized IPs, the virtualized regions are loaded with blank bitstreams to save power. However, keeping active the idle components of the NoC connecting with the idle virtualized regions is …
Scalable and Selective Preparation of 3,3′,5,5′-Tetramethyl-2,2′-biphenol
2016
Biphenols are indispensable building blocks in ligand systems for organic catalysis. 3,3′5,5′-Tetramethyl-2,2′-biphenol is a particular versatile motif in different catalytic systems. We developed an easy to perform and scalable process to give access to large quantities of this important building block by the use of selenium dioxide, a common and readily available oxidizer.
Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud
2020
Remote sensing optical sensors onboard operational satellites cannot have high spectral, spatial and temporal resolutions simultaneously. In addition, clouds and aerosols can adversely affect the signal contaminating the land surface observations. We present a HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) algorithm to combine multispectral images of different sensors to reduce noise and produce monthly gap free high resolution (30 m) observations over land. Our approach uses images from the Landsat (30 m spatial resolution and 16 day revisit cycle) and the MODIS missions, both from Terra and Aqua platforms (500 m spatial resolution and daily revisit cycle). We implem…
Cloud detection on the Google Earth engine platform
2017
The vast amount of data acquired by current high resolution Earth observation satellites implies some technical challenges to be faced. Google Earth Engine (GEE) platform provides a framework for the development of algorithms and products built over this data in an easy and scalable manner. In this paper, we take advantage of the GEE platform capabilities to exploit the wealth of information in the temporal dimension by processing a long time series of satellite images. A cloud detection algorithm for Landsat-8, which uses previous images of the same location to detect clouds, is implemented and tested on the GEE platform.
Hybrid P2P schemes for remote terrain interactive visualization systems
2013
Over the last few years, there has been a lot of development of interactive terrain visualization applications using remote databases. One of the main problems that these applications must face is scalability. These applications usually use a client-server model that cannot support a large number of concurrent requests without using a considerable number of servers. In this paper, we present a full comparative study of new hybrid P2P schemes for terrain interactive visualization systems. The performance evaluation results show that the best strategy consists of avoiding the periodical reporting among peer nodes about the current information contained in each node, while using some servers a…
Massively Parallel ANS Decoding on GPUs
2019
In recent years, graphics processors have enabled significant advances in the fields of big data and streamed deep learning. In order to keep control of rapidly growing amounts of data and to achieve sufficient throughput rates, compression features are a key part of many applications including popular deep learning pipelines. However, as most of the respective APIs rely on CPU-based preprocessing for decoding, data decompression frequently becomes a bottleneck in accelerated compute systems. This establishes the need for efficient GPU-based solutions for decompression. Asymmetric numeral systems (ANS) represent a modern approach to entropy coding, combining superior compression results wit…
Fault-Tolerant Network-on-Chip Design for Mesh-of-Tree Topology Using Particle Swarm Optimization
2018
As the size of the chip is scaling down the density of Intellectual Property (IP) cores integrated on a chip has been increased rapidly. The communication between these IP cores on a chip is highly challenging. To overcome this issue, Network-on-Chip (NoC) has been proposed to provide an efficient and a scalable communication architecture. In the deep sub-micron level NoCs are prone to faults which can occur in any component of NoC. To build a reliable and robust systems, it is necessary to apply efficient fault-tolerant techniques. In this paper, we present a flexible spare core placement in Mesh-of-Tree (MoT) topology using Particle Swarm Optimization (PSO) by considering IP core failures…
WarpDrive: Massively Parallel Hashing on Multi-GPU Nodes
2018
Hash maps are among the most versatile data structures in computer science because of their compact data layout and expected constant time complexity for insertion and querying. However, associated memory access patterns during the probing phase are highly irregular resulting in strongly memory-bound implementations. Massively parallel accelerators such as CUDA-enabled GPUs may overcome this limitation by virtue of their fast video memory featuring almost one TB/s bandwidth in comparison to main memory modules of state-of-the-art CPUs with less than 100 GB/s. Unfortunately, the size of hash maps supported by existing single-GPU hashing implementations is restricted by the limited amount of …
SWMapper: Scalable Read Mapper on SunWay TaihuLight
2020
With the rapid development of next-generation sequencing (NGS) technologies, high throughput sequencing platforms continuously produce large amounts of short read DNA data at low cost. Read mapping is a performance-critical task, being one of the first stages required for many different types of NGS analysis pipelines. We present SWMapper — a scalable and efficient read mapper for the Sunway TaihuLight supercomputer. A number of optimization techniques are proposed to achieve high performance on its heterogeneous architecture which are centered around a memory-efficient succinct hash index data structure including seed filtration, duplicate removal, dynamic scheduling, asynchronous data tra…