scholarly journals A Survey on Domain-Specific Memory Architectures

2021 ◽  
Vol 16 (2) ◽  
pp. 1-9
Author(s):  
Stephanie Soldavini ◽  
Christian Pilato
Keyword(s):  
High Performance ◽  
Current Trend ◽  
Security And Privacy ◽  
Graph Analytics ◽  
Domain Specific ◽  
Specific Memory ◽  
Memory Modules ◽  
Proper Design ◽  
Efficient Memory ◽  
Memory Architectures

The never-ending demand for high performance and energy efficiency is pushing designers towards an increasing level of heterogeneity and specialization in modern computing systems. In such systems, creating efficient memory architectures is one of the major opportunities for optimizing modern workloads (e.g., computer vision, machine learning, graph analytics, etc.) that are extremely data-driven. However, designers demand proper design methods to tackle the increasing design complexity and address several new challenges, like the security and privacy of the data to be elaborated.This paper overviews the current trend for the design of domain-specific memory architectures. Domain-specific architectures are tailored for the given application domain, with the introduction of hardware accelerators and custom memory modules while maintaining a certain level of flexibility. We describe the major components, the common challenges, and the state-of-the-art design methodologies for building domain-specific memory architectures. We also discuss the most relevant research projects, providing a classification based on our main topics.

Coding Productivity in MapReduce Applications for Distributed and Shared Memory Architectures

2015 ◽  
Vol 25 (09n10) ◽  
pp. 1739-1741
Author(s):  
Daniel Adornes ◽  
Dalvan Griebler ◽  
Cleverson Ledur ◽  
Luiz Gustavo Fernandes
Keyword(s):  
Shared Memory ◽  
Code Generation ◽  
High Performance ◽  
Domain Specific ◽  
Significant Performance ◽  
Memory Architectures ◽  
Programming Interfaces ◽  
Shared Memory Architectures ◽  
Performance Computing

MapReduce was originally proposed as a suitable and efficient approach for analyzing and processing large amounts of data. Since then, many researches contributed with MapReduce implementations for distributed and shared memory architectures. Nevertheless, different architectural levels require different optimization strategies in order to achieve high-performance computing. Such strategies in turn have caused very different MapReduce programming interfaces among these researches. This paper presents some research notes on coding productivity when developing MapReduce applications for distributed and shared memory architectures. As a case study, we introduce our current research on a unified MapReduce domain-specific language with code generation for Hadoop and Phoenix++, which has achieved a coding productivity increase from 41.84% and up to 94.71% without significant performance losses (below 3%) compared to those frameworks.

APA Book Talk: The Psychology of High Performance: Developing Human Potential Into Domain-Specific Talent

2020 ◽  
Author(s):  
Jamie Buck ◽  
Rena Subotnik ◽  
Frank Worrell ◽  
Paula Olszewski-Kubilius ◽  
Chi Wang
Keyword(s):  
High Performance ◽  
Human Potential ◽  
Domain Specific ◽  
Book Talk

Synthesis of Distributed Compliant Mechanisms for Adaptive Structures Application: An Elasto-Kinematic Approach

1997 ◽  
Author(s):  
Laxminarayana Saggere ◽  
Sridhar Kota
Keyword(s):  
Smart Materials ◽  
High Performance ◽  
Compliant Mechanisms ◽  
Shape Change ◽  
Current Trend ◽  
Adaptive Structures ◽  
Topology Generation ◽  
Synthesis Procedure ◽  
Actuation Scheme ◽  
Kinematic Approach

Abstract Compliant mechanisms are a class of mechanisms that achieve desired force and motion transmission tasks by undergoing elastic deformations as opposed to rigid-body displacements in the conventional rigid-link mechanisms. Most of the previously reported synthesis studies in compliant mechanisms related to either partially-compliant mechanisms or fully-compliant mechanisms with joint compliance. Methods developed for fully-compliant mechanisms with link compliance addressed the issue of topology generation for desired deflections at discrete points on the mechanism. This paper presents a new, first-principles based synthesis procedure for fully-compliant mechanisms with link compliance — that is, distributed-compliant mechanisms — for continuous shape change requirements in a particular segment of a mechanism. The general approach presented in this paper for the synthesis of distributed compliant mechanisms is shown to be well suited for application in the design of adaptive structures, an emerging class of high-performance structural systems. The current trend in the design of adaptive structures is to embed structures with force or strain inducing “smart” materials to serve as distributed actuators. Potential advantages of using the distributed compliance scheme over the distributed actuation scheme in the design of adaptive structures include a significant reduction in the number of required actuators and controls.

Octopus + : An RDMA-Enabled Distributed Persistent Memory File System

2021 ◽  
Vol 17 (3) ◽  
pp. 1-25
Author(s):  
Bohong Zhu ◽  
Youmin Chen ◽  
Qing Wang ◽  
Youyou Lu ◽  
Jiwu Shu
Keyword(s):  
High Speed ◽  
High Performance ◽  
File System ◽  
Direct Memory Access ◽  
File Systems ◽  
Distributed File Systems ◽  
Persistent Memory ◽  
Memory Modules ◽  
Non Volatile Memory ◽  
Volatile Memory

Non-volatile memory and remote direct memory access (RDMA) provide extremely high performance in storage and network hardware. However, existing distributed file systems strictly isolate file system and network layers, and the heavy layered software designs leave high-speed hardware under-exploited. In this article, we propose an RDMA-enabled distributed persistent memory file system, Octopus + , to redesign file system internal mechanisms by closely coupling non-volatile memory and RDMA features. For data operations, Octopus + directly accesses a shared persistent memory pool to reduce memory copying overhead, and actively fetches and pushes data all in clients to rebalance the load between the server and network. For metadata operations, Octopus + introduces self-identified remote procedure calls for immediate notification between file systems and networking, and an efficient distributed transaction mechanism for consistency. Octopus + is enabled with replication feature to provide better availability. Evaluations on Intel Optane DC Persistent Memory Modules show that Octopus + achieves nearly the raw bandwidth for large I/Os and orders of magnitude better performance than existing distributed file systems.

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.

Falcon: A Domain Specific Language for Graph Analytics

2020 ◽  
pp. 153-179
Author(s):  
Unnikrishnan Cheramangalath ◽  
Rupesh Nasre ◽  
Y. N. Srikant
Keyword(s):  
Domain Specific Language ◽  
Graph Analytics ◽  
Specific Language ◽  
Domain Specific

scholarly journals TaxoNERD: deep neural models for the recognition of taxonomic entities in the ecological and evolutionary literature

2021 ◽  
Author(s):  
Nicolas Le Guillarme ◽  
Wilfried Thuiller
Keyword(s):  
Information Extraction ◽  
High Performance ◽  
Named Entity Recognition ◽  
Open Data ◽  
Entity Recognition ◽  
Data Repositories ◽  
Biodiversity Crisis ◽  
Domain Specific ◽  
Ecological Information ◽  
Gold Standard Corpus

1. Given the biodiversity crisis, we more than ever need to access information on multiple taxa (e.g. distribution, traits, diet) in the scientific literature to understand, map and predict all-inclusive biodiversity. Tools are needed to automatically extract useful information from the ever-growing corpus of ecological texts and feed this information to open data repositories. A prerequisite is the ability to recognise mentions of taxa in text, a special case of named entity recognition (NER). In recent years, deep learning-based NER systems have become ubiqutous, yielding state-of-the-art results in the general and biomedical domains. However, no such tool is available to ecologists wishing to extract information from the biodiversity literature. 2. We propose a new tool called TaxoNERD that provides two deep neural network (DNN) models to recognise taxon mentions in ecological documents. To achieve high performance, DNN-based NER models usually need to be trained on a large corpus of manually annotated text. Creating such a gold standard corpus (GSC) is a laborious and costly process, with the result that GSCs in the ecological domain tend to be too small to learn an accurate DNN model from scratch. To address this issue, we leverage existing DNN models pretrained on large biomedical corpora using transfer learning. The performance of our models is evaluated on four GSCs and compared to the most popular taxonomic NER tools. 3. Our experiments suggest that existing taxonomic NER tools are not suited to the extraction of ecological information from text as they performed poorly on ecologically-oriented corpora, either because they do not take account of the variability of taxon naming practices, or because they do not generalise well to the ecological domain. Conversely, a domain-specific DNN-based tool like TaxoNERD outperformed the other approaches on an ecological information extraction task. 4. Efforts are needed in order to raise ecological information extraction to the same level of performance as its biomedical counterpart. One promising direction is to leverage the huge corpus of unlabelled ecological texts to learn a language representation model that could benefit downstream tasks. These efforts could be highly beneficial to ecologists on the long term.

scholarly journals Forensic Feature-Comparison Expertise: Statistical Learning Facilitates Visual Comparison Performance

2020 ◽  
Author(s):  
Bethany Growns ◽  
Kristy Martire
Keyword(s):  
Statistical Learning ◽  
High Performance ◽  
Comparison Task ◽  
Domain Specific ◽  
Visual Evidence ◽  
Visual Comparison ◽  
Distributional Information ◽  
Distributional Learning ◽  
The Impact ◽  
The Relationship

Forensic feature-comparison examiners in select disciplines are more accurate than novices when comparing visual evidence samples. This paper examines a key cognitive mechanism that may contribute to this superior visual comparison performance: the ability to learn how often stimuli occur in the environment (distributional statistical learning). We examined the relation-ship between distributional learning and visual comparison performance, and the impact of training about the diagnosticity of distributional information in visual comparison tasks. We compared performance between novices given no training (uninformed novices; n = 32), accu-rate training (informed novices; n = 32) or inaccurate training (misinformed novices; n = 32) in Experiment 1; and between forensic examiners (n = 26), informed novices (n = 29) and unin-formed novices (n = 27) in Experiment 2. Across both experiments, forensic examiners and nov-ices performed significantly above chance in a visual comparison task where distributional learning was required for high performance. However, informed novices outperformed all par-ticipants and only their visual comparison performance was significantly associated with their distributional learning. It is likely that forensic examiners’ expertise is domain-specific and doesn’t generalise to novel visual comparison tasks. Nevertheless, diagnosticity training could be critical to the relationship between distributional learning and visual comparison performance.

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