scholarly journals PRISM

2021 ◽  
Vol 18 (3) ◽  
pp. 1-25
Author(s):  
Hamza Omar ◽  
Omer Khan
Keyword(s):  
Resource Sharing ◽  
Data Access ◽  
Soft Error ◽  
Parallel Applications ◽  
Interference Effects ◽  
Efficient System ◽  
System Availability ◽  
Safety Standards ◽  
Redundant Execution ◽  
On Chip

Multicores increasingly deploy safety-critical parallel applications that demand resiliency against soft-errors to satisfy the safety standards. However, protection against these errors is challenging due to complex communication and data access protocols that aggressively share on-chip hardware resources. Research has explored various temporal and spatial redundancy-based resiliency schemes that provide multicores with high soft-error coverage. However, redundant execution incurs performance overheads due to interference effects induced by aggressive resource sharing. Moreover, these schemes require intrusive hardware modifications and fall short in providing efficient system availability guarantees. This article proposes PRISM, a resilient multicore architecture that incorporates strong hardware isolation to form redundant clusters of cores, ensuring a non-interference-based redundant execution environment. A soft error in one cluster does not effect the execution of the other cluster, resulting in high system availability. Implementing strong isolation for shared hardware resources, such as queues, caches, and networks requires logic for partitioning. However, it is less intrusive as complex hardware modifications to protocols, such as hardware cache coherence, are avoided. The PRISM approach is prototyped on a real Tilera Tile-Gx72 processor that enables primitives to implement the proposed cluster-level hardware resource isolation. The evaluation shows performance benefits from avoiding destructive hardware interference effects with redundant execution, while delivering superior system availability.

A soft-error-immune 0.9-ns 1.15-Mb ECL-CMOS SRAM with 30-ps 120 k logic gates and on-chip test circuitry

1996 ◽  
Vol 31 (10) ◽  
pp. 1443-1450 ◽  
Author(s):  
K. Higeta ◽  
M. Usami ◽  
M. Ohayashi ◽  
Y. Fujimura ◽  
M. Nishiyama ◽  
...  
Keyword(s):  
Logic Gates ◽  
Soft Error ◽  
On Chip

scholarly journals Characterizing and Mitigating Work Time Inflation in Task Parallel Programs

2013 ◽  
Vol 21 (3-4) ◽  
pp. 123-136 ◽  
Author(s):  
Stephen L. Olivier ◽  
Bronis R. de Supinski ◽  
Martin Schulz ◽  
Jan F. Prins
Keyword(s):  
Poor Performance ◽  
Data Access ◽  
Parallel Applications ◽  
Task Parallelism ◽  
Improve Performance ◽  
Additional Time ◽  
Work Time ◽  
Task Parallel ◽  
Time Required ◽  
Data Access Latency

Task parallelism raises the level of abstraction in shared memory parallel programming to simplify the development of complex applications. However, task parallel applications can exhibit poor performance due to thread idleness, scheduling overheads, andwork time inflation– additional time spent by threads in a multithreaded computation beyond the time required to perform the same work in a sequential computation. We identify the contributions of each factor to lost efficiency in various task parallel OpenMP applications and diagnose the causes of work time inflation in those applications. Increased data access latency can cause significant work time inflation in NUMA systems. Our locality framework for task parallel OpenMP programs mitigates this cause of work time inflation. Our extensions to the Qthreads library demonstrate that locality-aware scheduling can improve performance up to 3X compared to the Intel OpenMP task scheduler.

scholarly journals Automating Privacy Compliance Using Policy Integrated Blockchain

Cryptography ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 7 ◽  
Author(s):  
Karuna Pande Joshi ◽  
Agniva Banerjee
Keyword(s):  
Data Sharing ◽  
Data Privacy ◽  
Personal Data ◽  
Data Access ◽  
Information Management System ◽  
Knowledge Graph ◽  
Efficient System ◽  
Essential Requirement ◽  
Blockchain Technology ◽  
Rich Data

An essential requirement of any information management system is to protect data and resources against breach or improper modifications, while at the same time ensuring data access to legitimate users. Systems handling personal data are mandated to track its flow to comply with data protection regulations. We have built a novel framework that integrates semantically rich data privacy knowledge graph with Hyperledger Fabric blockchain technology, to develop an automated access-control and audit mechanism that enforces users' data privacy policies while sharing their data with third parties. Our blockchain based data-sharing solution addresses two of the most critical challenges: transaction verification and permissioned data obfuscation. Our solution ensures accountability for data sharing in the cloud by incorporating a secure and efficient system for End-to-End provenance. In this paper, we describe this framework along with the comprehensive semantically rich knowledge graph that we have developed to capture rules embedded in data privacy policy documents. Our framework can be used by organizations to automate compliance of their Cloud datasets.

Replica Placement Strategy for Data Grid Environment

2013 ◽  
Vol 5 (1) ◽  
pp. 70-81 ◽  
Author(s):  
Mohammed K. Madi ◽  
Yuhanis Yusof ◽  
Suhaidi Hassan
Keyword(s):  
Resource Sharing ◽  
Data Access ◽  
Data Grid ◽  
Computational Time ◽  
Replica Placement ◽  
File Transfer ◽  
Large Bandwidth ◽  
Multiple Copies ◽  
Data Files ◽  
Computational Resources

Data Grid is an infrastructure that manages huge amount of data files, and provides intensive computational resources across geographically distributed collaboration. To increase resource availability and to ease resource sharing in such environment, there is a need for replication services. Data replication is one of the methods used to improve the performance of data access in distributed systems by replicating multiple copies of data files in the distributed sites. Replica placement mechanism is the process of identifying where to place copies of replicated data files in a Grid system. Existing work identifies the suitable sites based on number of requests and read cost of the required file. Such approaches consume large bandwidth and increases the computational time. The authors propose a replica placement strategy (RPS) that finds the best locations to store replicas based on four criteria, namely, 1) Read Cost, 2) File Transfer Time, 3) Sites’ Workload, and 4) Replication Sites. OptorSim is used to evaluate the performance of this replica placement strategy. The simulation results show that RPS requires less execution time and consumes less network usage compared to existing approaches of Simple Optimizer and LFU (Least Frequently Used).

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