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A GPU-Accelerated in-memory Metadata Management Scheme forLarge-Scale Parallel File Systems

Journal of Computer Science & Technology 2021年第1期36卷 44-55页

作者： Zhi-Guang Chen Yu-Bo Liu Yong-Feng Wang Yu-Tong Lu School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China

Driven by the increasing requirements of high-performance computing applications,supercomputers are prone to containing more and more computing *** running on such a large-scale computing system are likely to spawn millions of parallel processes,which usually generate a burst of I/O requests,introducing a great challenge into the metadata management of underlying parallel file *** traditional method used to overcome such a challenge is adopting multiple metadata servers in the scale-out manner,which will inevitably confront with serious network and consistence *** work instead pursues to enhance the metadata performance in the scale-up ***,we propose to improve the performance of each individual metadata server by employing GPU to handle metadata requests in *** proposal designs a novel metadata server architecture,which employs CPU to interact with file system clients,while offloading the computing tasks about metadata into *** take full advantages of the parallelism existing in GPU,we redesign the in-memory data structure for the name space of file *** new data structure can perfectly fit to the memory architecture of GPU,and thus helps to exploit the large number of parallel threads within GPU to serve the bursty metadata requests *** implement a prototype based on BeeGFS and conduct extensive experiments to evaluate our proposal,and the experimental results demonstrate that our GPU-based solution outperforms the CPU-based scheme by more than 50%under typical metadata *** superiority is strengthened further on high concurrent scenarios,e.g.,the high-performance computing systems supporting millions of parallel threads.

关键词： GPU-accelerated in-memory metadata management parallel file system

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Memway:in-memory Waylaying Acceleration for Practical Rowhammer Attacks Against Binaries

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Tsinghua Science and Technology 2019年第5期24卷 535-545页

作者： Lai Xu Rongwei Yu Lina Wang Weijie Liu the Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University

The Rowhammer bug is a novel micro-architectural security threat, enabling powerful privilege-escalation attacks on various mainstream platforms. It works by actively flipping bits in Dynamic Random Access memory(DRAM) cells with unprivileged instructions. In order to set up Rowhammer against binaries in the Linux page cache, the Waylaying algorithm has previously been proposed. The Waylaying method stealthily relocates binaries onto exploitable physical addresses without exhausting system memory. However, the proof-of-concept Waylaying algorithm can be easily detected during page cache eviction because of its high disk I/O overhead and long running time. This paper proposes the more advanced Memway algorithm, which improves on Waylaying in terms of both I/O overhead and speed. Running time and disk I/O overhead are reduced by 90% by utilizing Linux tmpfs and inmemory swapping to manage eviction files. Furthermore, by combining Memway with the unprivileged posix fadvise API, the binary relocation step is made 100 times faster. Equipped with our Memway+fadvise relocation scheme,we demonstrate practical Rowhammer attacks that take only 15–200 minutes to covertly relocate a victim binary,and less than 3 seconds to flip the target instruction bit.

关键词： Rowhammer bug Waylaying algorithm in-memory swapping page cache eviction

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SAP S/4HANA Embedded Analytics: An Overview

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Journal of Computer and Communications 2017年第9期5卷 1-7页

作者： Abani Pattanayak SAP HANA Center of Excellence SAP America Dallas USA

Operational analytics is all about answering business questions while doing business and supporting business users across the organization, from shop floor users to management and executives. Therefore, business transactions and analytics must co-exist together in a single platform to empower business users to drive insights, make decisions, and complete business processes in a single application and using a single source of facts without toggling between multiple applications. Traditionally transactional systems and analytics were maintained separately to improve throughput of the transactional system and that certainly introduced latency in decision making. However, with innovation in the SAP HANA platform, SAP S/4HANA embedded analytics enables business users, business analysts, and management to perform real-time analytics on live transactional data. This paper reviews technical architecture and key components of SAP S/4HANA embedded analytics. This paper reviews technical architecture and key components of SAP S/4HANA embedded analytics.

关键词： SAP HANA in-memory Computing S/4HANA Embedded Analytics

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High Performance Analytics with SAP HANA Virtual Models

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Journal of Computer and Communications 2017年第7期5卷 1-10页

作者： Abani Pattanayak SAP HANA Center of Excellence SAP America Dallas TX USA

Informed decision-making, better communication and faster response to business situation are the key differences between leaders and followers in this competitive global marketplace. A data-driven organization can analyze patterns & anomalies to make sense of the current situation and be ready for future opportunities. Organizations no longer have the problem of “lack of data”, but the problem of “actionable data” at the right time to act, direct and influence their business decisions. The data exists in different transactional systems and/or data warehouse systems, which takes significant time to retrieve/ process relevant information and negatively impacts the time window to out-maneuver the competition. To solve the problem of “actionable data”, enterprises can take advantage of the SAP HANA [1] in-memory platform that enables rapid processing and analysis of huge volumes of data in real-time. This paper discusses how SAP HANA virtual data models can be used for on-the-fly analysis of live transactional data to derive insight, perform what-if analysis and execute business transactions in real-time without using persisted aggregates.

关键词： SAP HANA in-memory Computing Virtual Data Model Advanced Analytics Big Data

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Data Virtualization with SAP HANA Smart Data Access

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Journal of Computer and Communications 2017年第8期5卷 62-68页

作者： Abani Pattanayak SAP HANA Center of Excellence SAP America Dallas USA

Digital transformation has been corner stone of business innovation in the last decade, and these innovations have dramatically changed the definition and boundaries of enterprise business applications. Introduction of new products/ services, version management of existing products/ services, management of customer/partner connections, management of multi-channel service delivery (web, social media, web etc.), merger/acquisitions of new businesses and adoption of new innovations/technologies will drive data growth in business applications. These datasets exist in different sharing nothing business applications at different locations and in various forms. So, to make sense of this information and derive insight, it is essential to break the data silos, streamline data retrieval and simplify information access across the entire organization. The information access framework must support just-in-time processing capabilities to bring data from multiple sources, be fast and powerful enough to transform and process huge amounts of data quickly, and be agile enough to accommodate new data sources per user needs. This paper discusses the SAP HANA Smart Data Access data-virtualization technology to enable unified access to heterogenous data across the organization and analysis of huge volume of data in real-time using SAP HANA in-memory platform.

关键词： SAP HANA in-memory Computing Smart Data Access (SDA) Data Virtualization & Data Federation Virtual Data Model And Big Data

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