scholarly journals Early Address Prediction

2021 ◽  
Vol 18 (3) ◽  
pp. 1-22
Author(s):  
Ricardo Alves ◽  
Stefanos Kaxiras ◽  
David Black-Schaffer
Keyword(s):  
Data Storage ◽  
Additional Data ◽  
State Of The Art ◽  
Data Reuse ◽  
Spatial Reuse ◽  
Register File ◽  
Data Movement ◽  
Low Load ◽  
Prefetching Technique ◽  
And Storage

Achieving low load-to-use latency with low energy and storage overheads is critical for performance. Existing techniques either prefetch into the pipeline (via address prediction and validation) or provide data reuse in the pipeline (via register sharing or L0 caches). These techniques provide a range of tradeoffs between latency, reuse, and overhead. In this work, we present a pipeline prefetching technique that achieves state-of-the-art performance and data reuse without additional data storage, data movement, or validation overheads by adding address tags to the register file. Our addition of register file tags allows us to forward (reuse) load data from the register file with no additional data movement, keep the data alive in the register file beyond the instruction’s lifetime to increase temporal reuse, and coalesce prefetch requests to achieve spatial reuse. Further, we show that we can use the existing memory order violation detection hardware to validate prefetches and data forwards without additional overhead. Our design achieves the performance of existing pipeline prefetching while also forwarding 32% of the loads from the register file (compared to 15% in state-of-the-art register sharing), delivering a 16% reduction in L1 dynamic energy (1.6% total processor energy), with an area overhead of less than 0.5%.

Mix and Match: Reorganizing Tasks for Enhancing Data Locality

2021 ◽  
Vol 5 (2) ◽  
pp. 1-24
Author(s):  
Xulong Tang ◽  
Mahmut Taylan Kandemir ◽  
Mustafa Karakoy
Keyword(s):  
Spatial Data ◽  
State Of The Art ◽  
Data Access ◽  
Data Locality ◽  
Data Reuse ◽  
Large Set ◽  
Data Movement ◽  
High Data ◽  
Multithreaded Programs ◽  
Data Location

Application programs that exhibit strong locality of reference lead to minimized cache misses and better performance in different architectures. However, to maximize the performance of multithreaded applications running on emerging manycore systems, data movement in on-chip network should also be minimized. Unfortunately, the way many multithreaded programs are written does not lend itself well to minimal data movement. Motivated by this observation, in this paper, we target task-based programs (which cover a large set of available multithreaded programs), and propose a novel compiler-based approach that consists of four complementary steps. First, we partition the original tasks in the target application into sub-tasks and build a data reuse graph at a sub-task granularity. Second, based on the intensity of temporal and spatial data reuses among sub-tasks, we generate new tasks where each such (new) task includes a set of sub-tasks that exhibit high data reuse among them. Third, we assign the newly-generated tasks to cores in an architecture-aware fashion with the knowledge of data location. Finally, we re-schedule the execution order of sub-tasks within new tasks such that sub-tasks that belong to different tasks but share data among them are executed in close proximity in time. The detailed experiments show that, when targeting a state of the art manycore system, our proposed compiler-based approach improves the performance of 10 multithreaded programs by 23.4% on average, and it also outperforms two state-of-the-art data access optimizations for all the benchmarks tested. Our results also show that the proposed approach i) improves the performance of multiprogrammed workloads, and ii) generates results that are close to maximum savings that could be achieved with perfect profiling information. Overall, our experimental results emphasize the importance of dividing an original set of tasks of an application into sub-tasks and constructing new tasks from the resulting sub-tasks in a data movement- and locality-aware fashion.

Chemometrics

2019 ◽  
Vol 2 (1) ◽  
pp. 1-42 ◽  
Author(s):  
Gerard G. Dumancas ◽  
Ghalib Bello ◽  
Jeff Hughes ◽  
Renita Murimi ◽  
Lakshmi Viswanath ◽  
...  
Keyword(s):  
Analytical Chemistry ◽  
Big Data ◽  
Data Storage ◽  
State Of The Art ◽  
Fog Computing ◽  
Big Data Analytics ◽  
Computer Applications ◽  
Analytical Instruments ◽  
Big Data Visualization ◽  
And Storage

The accumulation of data from various instrumental analytical instruments has paved a way for the application of chemometrics. Challenges, however, exist in processing, analyzing, visualizing, and storing these data. Chemometrics is a relatively young area of analytical chemistry that involves the use of statistics and computer applications in chemistry. This article will discuss various computational and storage tools of big data analytics within the context of analytical chemistry with examples, applications, and usage details in relation to fog computing. The future of fog computing in chemometrics will also be discussed. The article will dedicate particular emphasis to preprocessing techniques, statistical and machine learning methodology for data mining and analysis, tools for big data visualization, and state-of-the-art applications for data storage using fog computing.

A System Architecture for Data Storage in the Cloud

2015 ◽  
Vol 3 (2) ◽  
Author(s):  
Maryam Hammami ◽  
Hatem Bellaaj
Keyword(s):  
Data Storage ◽  
System Architecture ◽  
Cloud Storage ◽  
The Other ◽  
Important Data ◽  
Work In Progress ◽  
Flexible System ◽  
Other Hand ◽  
Data Manager ◽  
And Storage

The Cloud storage is the most important issue today. This is due to a rapidly changing needs and a huge mass of varied and important data to back up. In this paper, we describe a work in progress and propose a flexible system architecture for data storage in the Cloud. This system is centered on the Data Manager module. This module provides various functions such as the dispersion of data in fragments, encryption and storage of fragments... etc. This architecture proves to be very relevant. It ensures consistency between different components. On the other hand, it ensures the security and availability of data.

Design of FPGA-Based Missile-Borne Image Acquisition and Storage Recorder

2015 ◽  
Vol 713-715 ◽  
pp. 1448-1451
Author(s):  
Lin Lu ◽  
Yan Feng Zhang ◽  
Xiao Feng Li
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Storage System ◽  
Large Data ◽  
Engineering Practice ◽  
Image Storage ◽  
Video Images ◽  
Stable Performance ◽  
And Storage ◽  
Time Image

The high-altitude missile and other special application occasions have requirements on image storage system, such as small size, high storage speed, low temperature resistance, etc. Commonly used image storage system in the market cannot meet such requirement. In the paper, real-time image storage system solutions on missile based on FPGA should be proposed. The system mainly consists of acquisition module and memory reading module. The whole system adopts FPGA as main control chip for mainly completing real-time decoding and acquisition on one path of PAL format video images, reading and writing of NandFlash chipset, erasure, bad block management and so on. The solution has passed various environmental tests with stable performance, large data storage capacity and easy expansion, which has been used in engineering practice.

scholarly journals GIF IMAGE HARDWARE COMPRESSORS

2021 ◽  
Author(s):  
Ivan Mozghovyi ◽  
Anatoliy Sergiyenko ◽  
Roman Yershov
Keyword(s):  
Data Compression ◽  
Data Storage ◽  
High Speed ◽  
Data Transfer ◽  
Future Research ◽  
Volume Data ◽  
High Speed Data ◽  
Lzw Algorithm ◽  
And Storage ◽  
Speed Data Transmission

Increasing requirements for data transfer and storage is one of the crucial questions now. There are several ways of high-speed data transmission, but they meet limited requirements applied to their narrowly focused specific target. The data compression approach gives the solution to the problems of high-speed transfer and low-volume data storage. This paper is devoted to the compression of GIF images, using a modified LZW algorithm with a tree-based dictionary. It has led to a decrease in lookup time and an increase in the speed of data compression, and in turn, allows developing the method of constructing a hardware compression accelerator during the future research.

scholarly journals Hallucinating Optical Flow Features for Video Classification

2019 ◽  
Author(s):  
Yongyi Tang ◽  
Lin Ma ◽  
Lianqiang Zhou
Keyword(s):  
Optical Flow ◽  
Data Storage ◽  
Large Scale ◽  
State Of The Art ◽  
Video Content ◽  
Video Classification ◽  
Temporal Relationships ◽  
Optical Flow Computation ◽  
Flow Features ◽  
Optical Flow Features

Appearance and motion are two key components to depict and characterize the video content. Currently, the two-stream models have achieved state-of-the-art performances on video classification. However, extracting motion information, specifically in the form of optical flow features, is extremely computationally expensive, especially for large-scale video classification. In this paper, we propose a motion hallucination network, namely MoNet, to imagine the optical flow features from the appearance features, with no reliance on the optical flow computation. Specifically, MoNet models the temporal relationships of the appearance features and exploits the contextual relationships of the optical flow features with concurrent connections. Extensive experimental results demonstrate that the proposed MoNet can effectively and efficiently hallucinate the optical flow features, which together with the appearance features consistently improve the video classification performances. Moreover, MoNet can help cutting down almost a half of computational and data-storage burdens for the two-stream video classification. Our code is available at: https://github.com/YongyiTang92/MoNet-Features

scholarly journals Entering the Era of Data Science: Targeted Learning and the Integration of Statistics and Computational Data Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Mark J. van der Laan ◽  
Richard J. C. M. Starmans
Keyword(s):  
Big Data ◽  
Data Science ◽  
State Of The Art ◽  
Accurate Estimation ◽  
Learning Tools ◽  
New Developments ◽  
Data Movement ◽  
Computational Data ◽  
Adaptive Estimators ◽  
Data Adaptive

This outlook paper reviews the research of van der Laan’s group on Targeted Learning, a subfield of statistics that is concerned with the construction of data adaptive estimators of user-supplied target parameters of the probability distribution of the data and corresponding confidence intervals, aiming at only relying on realistic statistical assumptions. Targeted Learning fully utilizes the state of the art in machine learning tools, while still preserving the important identity of statistics as a field that is concerned with both accurate estimation of the true target parameter value and assessment of uncertainty in order to make sound statistical conclusions. We also provide a philosophical historical perspective on Targeted Learning, also relating it to the new developments in Big Data. We conclude with some remarks explaining the immediate relevance of Targeted Learning to the current Big Data movement.

scholarly journals Enhancement of Electrocatalytic Oxygen Evolution by Chiral Molecular Functionalization of Hybrid 2D Electrodes

2021 ◽  
Author(s):  
Yunchang Liang ◽  
Karla Banjac ◽  
Kévin Martin ◽  
Nicolas Zigon ◽  
Seunghwa Lee ◽  
...  
Keyword(s):  
Binding Energy ◽  
Oxygen Evolution ◽  
State Of The Art ◽  
Chiral Molecules ◽  
Surface Binding ◽  
Surface Binding Energy ◽  
Energy Conversion And Storage ◽  
Polarization Mechanism ◽  
And Storage ◽  
Unconventional Methods

A sustainable future requires highly efficient energy conversion and storage processes, where electrocatalysis plays a crucial role. The activity of an electrocatalyst is governed by the binding energy towards the reaction intermediates, while the scaling relationships prevent the improvement of a catalytic system over its volcano-plot limits. To overcome these limitations, unconventional methods that are not fully determined by the surface binding energy can be helpful. Here, we use organic chiral molecules, i.e., hetero-helicenes, to boost the oxygen evolution reaction (OER) by ca. 131.5 % (at the potential of 1.65 V vs. RHE) at state-of-the-art 2D catalysts via a spin-polarization mechanism. Our results show that chiral molecule-functionalization is able to increase the OER activity of catalysts beyond the volcano limits. A guideline for optimizing the catalytic activity via chiral molecular functionalization of hybrid 2D electrodes is given.

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