Balazs Gerofi - IEEE Xplore Author Profile
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Balazs Gerofi

Publications
25
Citations
242
Publications by Year
20102024
Co-Authors:
Show All Co-Authors (96)
Image of author Balazs Gerofi

Balazs Gerofi

Affiliation

Supercompute Platforms Group
Intel Corporation
Santa Clara, CA, USA
High Performance Artificial Intelligence Systems Research Team
RIKEN Center for Computational Science
Kobe, Japan

Publication Topics

Biography

Balazs Gerofi received the M.Sc. degree in computer science from the Vrije Universiteit Amsterdam, Amsterdam, The Netherlands and the Ph.D. degree in computer science from The University of Tokyo, Tokyo, Japan.,He is a Research Scientist at the RIKEN Advanced Institute for Computational Science, Kobe, Japan, where he is primarily involved with system software development for high-performance computing. His research is mainly focused on operating systems, high-performance computing, cloud computing, and fault-tolerant computing.,Dr. Gerofi is a member of the IEEE Computer Society and the Association for Computing Machinery (ACM).(Based on document published on 26 September 2016).
Publications
25
Citations
242
Publications by Year
20102024
Co-Authors:
Show All Co-Authors (96)
Author's Published Works

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Page Type-Aware Full-Sequence Program Scheduling via Reinforcement Learning in High Density SSDs

Jun Li;Zhigang Cai;Balazs Gerofi;Yutaka Ishikawa;Jianwei Liao

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Year: 2024 | Volume: 43, Issue: 11 | Journal Article |
Full-sequence program (FSP) can program multiple bits simultaneously, and thus complete a multiple-page write at one time for naturally enhancing write performance of high density 3-D solid-state drives (SSDs). This article proposes an FSP scheduling approach for the 3-D quad-level cell (QLC) SSDs, to further boost their read responsiveness. Considering each FSP operation in QLC SSDs spans four di...Show More

Page Type-Aware Full-Sequence Program Scheduling via Reinforcement Learning in High Density SSDs

Jun Li;Zhigang Cai;Balazs Gerofi;Yutaka Ishikawa;Jianwei Liao

Year: 2024 | Volume: 43, Issue: 11 | Journal Article |

Adaptive Management With Request Granularity for DRAM Cache Inside nand-Based SSDs

Haodong Lin;Jun Li;Zhibing Sha;Zhigang Cai;Yuanquan Shi;Balazs Gerofi;Jianwei Liao

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Year: 2023 | Volume: 42, Issue: 8 | Journal Article |
Cited by: Papers (7)
Most flash-based solid-state drives (SSDs) adopt an onboard dynamic random access memory (DRAM) to buffer hot write data. Then, the write or overwrite operations can be absorbed by the DRAM cache, given that there is sufficient locality in the applications’ I/O access pattern, to consequently avoid flushing the write data onto underlying SSD cells. After analyzing typical real-world workloads over...Show More

Adaptive Management With Request Granularity for DRAM Cache Inside nand-Based SSDs

Haodong Lin;Jun Li;Zhibing Sha;Zhigang Cai;Yuanquan Shi;Balazs Gerofi;Jianwei Liao

Year: 2023 | Volume: 42, Issue: 8 | Journal Article |

Why Globally Re-shuffle? Revisiting Data Shuffling in Large Scale Deep Learning

Truong Thao Nguyen;François Trahay;Jens Domke;Aleksandr Drozd;Emil Vatai;Jianwei Liao;Mohamed Wahib;Balazs Gerofi

2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
Year: 2022 | Conference Paper |
Cited by: Papers (12)
Stochastic gradient descent (SGD) is the most prevalent algorithm for training Deep Neural Networks (DNN). SGD iterates the input data set in each training epoch processing data samples in a random access fashion. Because this puts enormous pressure on the I/O subsystem, the most common approach to distributed SGD in HPC environments is to replicate the entire dataset to node local SSDs. However, ...Show More

Why Globally Re-shuffle? Revisiting Data Shuffling in Large Scale Deep Learning

Truong Thao Nguyen;François Trahay;Jens Domke;Aleksandr Drozd;Emil Vatai;Jianwei Liao;Mohamed Wahib;Balazs Gerofi

Year: 2022 | Conference Paper |

Linux vs. Lightweight Multi-kernels for High Performance Computing: Experiences at Pre-Exascale

Balazs Gerofi;Kohei Tarumizu;Lei Zhang;Takayuki Okamoto;Masamichi Takagi;Shinji Sumimoto;Yutaka Ishikawa

SC21: International Conference for High Performance Computing, Networking, Storage and Analysis
Year: 2021 | Conference Paper |
The long standing consensus in the High-Performance Computing (HPC) Operating Systems (OS) community is that lightweight kernel (LWK) based OSes have the potential to outperform Linux at extreme scale. To explore if LWKs live up to their expectation we developed IHK/McKernel, a lightweight multi-kernel OS designed for HPC, and deployed it on two high-end supercomputers to compare its performance a...Show More

Linux vs. Lightweight Multi-kernels for High Performance Computing: Experiences at Pre-Exascale

Balazs Gerofi;Kohei Tarumizu;Lei Zhang;Takayuki Okamoto;Masamichi Takagi;Shinji Sumimoto;Yutaka Ishikawa

Year: 2021 | Conference Paper |

MLPerf™ HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems

Steven Farrell;Murali Emani;Jacob Balma;Lukas Drescher;Aleksandr Drozd;Andreas Fink;Geoffrey Fox;David Kanter;Thorsten Kurth;Peter Mattson;Dawei Mu;Amit Ruhela;Kento Sato;Koichi Shirahata;Tsuguchika Tabaru;Aristeidis Tsaris;Jan Balewski;Ben Cumming;Takumi Danjo;Jens Domke;Takaaki Fukai;Naoto Fukumoto;Tatsuya Fukushi;Balazs Gerofi;Takumi Honda;Toshiyuki Imamura;Akihiko Kasagi;Kentaro Kawakami;Shuhei Kudo;Akiyoshi Kuroda;Maxime Martinasso;Satoshi Matsuoka;Henrique Mendonça;Kazuki Minami;Prabhat Ram;Takashi Sawada;Mallikarjun Shankar;Tom St. John;Akihiro Tabuchi;Venkatram Vishwanath;Mohamed Wahib;Masafumi Yamazaki;Junqi Yin

2021 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC)
Year: 2021 | Conference Paper |
Cited by: Papers (13)
Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights. High performance computing systems are pushing the frontiers of performance with a rich diversity of hardware resources and massive scale-out capabilities. There is a critical need to understand fair and effective benchmarking of machine learning appli...Show More

MLPerf™ HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems

Steven Farrell;Murali Emani;Jacob Balma;Lukas Drescher;Aleksandr Drozd;Andreas Fink;Geoffrey Fox;David Kanter;Thorsten Kurth;Peter Mattson;Dawei Mu;Amit Ruhela;Kento Sato;Koichi Shirahata;Tsuguchika Tabaru;Aristeidis Tsaris;Jan Balewski;Ben Cumming;Takumi Danjo;Jens Domke;Takaaki Fukai;Naoto Fukumoto;Tatsuya Fukushi;Balazs Gerofi;Takumi Honda;Toshiyuki Imamura;Akihiko Kasagi;Kentaro Kawakami;Shuhei Kudo;Akiyoshi Kuroda;Maxime Martinasso;Satoshi Matsuoka;Henrique Mendonça;Kazuki Minami;Prabhat Ram;Takashi Sawada;Mallikarjun Shankar;Tom St. John;Akihiro Tabuchi;Venkatram Vishwanath;Mohamed Wahib;Masafumi Yamazaki;Junqi Yin

Year: 2021 | Conference Paper |

A Scalability Study of Data Exchange in HPC Multi-component Workflows

Jie Yin;Atsushi Hori;Balazs Gerofi;Yutaka Ishikawa

2021 IEEE International Conference on Cluster Computing (CLUSTER)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Multi-component workflows play a significant role in High-Performance Computing and Big Data applications. They usually contain multiple, independently developed components that execute side-by-side to perform sophisticated computation and data exchange through file I/O over parallel file system. However, file I/O can become an impediment in such systems and cause undesirable performance degradati...Show More

A Scalability Study of Data Exchange in HPC Multi-component Workflows

Jie Yin;Atsushi Hori;Balazs Gerofi;Yutaka Ishikawa

Year: 2021 | Conference Paper |

Application-Driven Requirements for Node Resource Management in Next-Generation Systems

Edgar A. León;Balazs Gerofi;Julien Jaeger;Guillaume Mercier;Rolf Riesen;Masamichi Takagi;Brice Goglin

2020 IEEE/ACM International Workshop on Runtime and Operating Systems for Supercomputers (ROSS)
Year: 2020 | Conference Paper |
Emerging workloads on supercomputing platforms are pushing the limits of traditional high-performance computing software environments. Multi-physics, coupled simulations, big data processing and machine learning frameworks, and multi-component workloads pose serious challenges to system and application developers. At the heart of the problem is the lack of cross-stack coordination to enable flexib...Show More

Application-Driven Requirements for Node Resource Management in Next-Generation Systems

Edgar A. León;Balazs Gerofi;Julien Jaeger;Guillaume Mercier;Rolf Riesen;Masamichi Takagi;Brice Goglin

Year: 2020 | Conference Paper |

An Implementation of User-Level Processes using Address Space Sharing

Atsushi Hori;Balazs Gerofi;Yutaka Ishikawa

2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
Year: 2020 | Conference Paper |
Cited by: Papers (2)
There is a wide range of implementation approaches to multi-threading. User-level threads are efficient because threads can be scheduled by a user-defined scheduling policy that suits the needs of the specific application. However, user-level threads are unable to handle blocking system-calls efficiently. To the contrary, kernel-level threads incur large overhead during context switching. Kernel-l...Show More

An Implementation of User-Level Processes using Address Space Sharing

Atsushi Hori;Balazs Gerofi;Yutaka Ishikawa

Year: 2020 | Conference Paper |

Parallel Multigrid Methods on Manycore Clusters with IHK/McKernel

Kengo Nakajima;Balazs Gerofi;Yutaka Ishikawa;Masashi Horikoshi

2019 IEEE/ACM 10th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA)
Year: 2019 | Conference Paper |
Cited by: Papers (1)
The parallel multigrid method is expected to play an important role in large-scale scientific computing on exa-scale supercomputer systems. Previously we proposed Hierarchical Coarse Grid Aggregation (hCGA), which dramatically improved the performance of the parallel multigrid solver when the number of MPI processes was O(104) or more. Because hCGA can handle only two layers of parallel hierarchic...Show More

Parallel Multigrid Methods on Manycore Clusters with IHK/McKernel

Kengo Nakajima;Balazs Gerofi;Yutaka Ishikawa;Masashi Horikoshi

Year: 2019 | Conference Paper |

Invited Talk 2

Balazs Gerofi

2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
Year: 2019 | Conference Paper |
Provides an abstract of the invited presentation and may include a brief professional biography of the presenter. The complete presentation was not made available for publication as part of the conference proceedings.Show More

Invited Talk 2

Balazs Gerofi

Year: 2019 | Conference Paper |

Performance and Scalability of Lightweight Multi-kernel Based Operating Systems

Balazs Gerofi;Rolf Riesen;Masamichi Takagi;Taisuke Boku;Kengo Nakajima;Yutaka Ishikawa;Robert W. Wisniewski

2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
Year: 2018 | Conference Paper |
Cited by: Papers (9)
Multi-kernels leverage today's multi-core chips to run multiple operating system (OS) kernels, typically a Light Weight Kernel (LWK) and a Linux kernel, simultaneously. The LWK provides high performance and scalability, while the Linux kernel provides compatibility. Multi-kernels show the promise of being able to meet tomorrow's extreme-scale computing needs while providing strong isolation, yield...Show More

Performance and Scalability of Lightweight Multi-kernel Based Operating Systems

Balazs Gerofi;Rolf Riesen;Masamichi Takagi;Taisuke Boku;Kengo Nakajima;Yutaka Ishikawa;Robert W. Wisniewski

Year: 2018 | Conference Paper |

Exploring Data Migration for Future Deep-Memory Many-Core Systems

Swann Perarnau;Judicael A. Zounmevo;Balazs Gerofi;Kamil Iskra;Pete Beckman

2016 IEEE International Conference on Cluster Computing (CLUSTER)
Year: 2016 | Conference Paper |
Cited by: Papers (7)
Upcoming high-performance computing (HPC) platforms will have more complex memory hierarchies with high-bandwidth on-package memory and in the future also non-volatile memory. How to use such deep memory hierarchies effectively remains an open research question. In this paper we evaluate the performance implications of a scheme based on a software-managed scratchpad with coarse-grained memory-copy...Show More

Exploring Data Migration for Future Deep-Memory Many-Core Systems

Swann Perarnau;Judicael A. Zounmevo;Balazs Gerofi;Kamil Iskra;Pete Beckman

Year: 2016 | Conference Paper |

“Big Data Assimilation” Toward Post-Petascale Severe Weather Prediction: An Overview and Progress

Takemasa Miyoshi;Guo-Yuan Lien;Shinsuke Satoh;Tomoo Ushio;Kotaro Bessho;Hirofumi Tomita;Seiya Nishizawa;Ryuji Yoshida;Sachiho A. Adachi;Jianwei Liao;Balazs Gerofi;Yutaka Ishikawa;Masaru Kunii;Juan Ruiz;Yasumitsu Maejima;Shigenori Otsuka;Michiko Otsuka;Kozo Okamoto;Hiromu Seko

Proceedings of the IEEE
Year: 2016 | Volume: 104, Issue: 11 | Journal Article |
Cited by: Papers (55)
Following the invention of the telegraph, electronic computer, and remote sensing, “big data” is bringing another revolution to weather prediction. As sensor and computer technologies advance, orders of magnitude bigger data are produced by new sensors and high-precision computer simulation or “big simulation.” Data assimilation (DA) is a key to numerical weather prediction (NWP) by integrating th...Show More

“Big Data Assimilation” Toward Post-Petascale Severe Weather Prediction: An Overview and Progress

Takemasa Miyoshi;Guo-Yuan Lien;Shinsuke Satoh;Tomoo Ushio;Kotaro Bessho;Hirofumi Tomita;Seiya Nishizawa;Ryuji Yoshida;Sachiho A. Adachi;Jianwei Liao;Balazs Gerofi;Yutaka Ishikawa;Masaru Kunii;Juan Ruiz;Yasumitsu Maejima;Shigenori Otsuka;Michiko Otsuka;Kozo Okamoto;Hiromu Seko

Year: 2016 | Volume: 104, Issue: 11 | Journal Article |

On the Scalability, Performance Isolation and Device Driver Transparency of the IHK/McKernel Hybrid Lightweight Kernel

Balazs Gerofi;Masamichi Takagi;Atsushi Hori;Gou Nakamura;Tomoki Shirasawa;Yutaka Ishikawa

2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
Year: 2016 | Conference Paper |
Cited by: Papers (32)
Extreme degree of parallelism in high-end computing requires low operating system noise so that large scale, bulk-synchronous parallel applications can be run efficiently. Noiseless execution has been historically achieved by deploying lightweight kernels (LWK), which, on the other hand, can provide only a restricted set of the POSIX API in exchange for scalability. However, the increasing prevale...Show More

On the Scalability, Performance Isolation and Device Driver Transparency of the IHK/McKernel Hybrid Lightweight Kernel

Balazs Gerofi;Masamichi Takagi;Atsushi Hori;Gou Nakamura;Tomoki Shirasawa;Yutaka Ishikawa

Year: 2016 | Conference Paper |

Prefetching on Storage Servers through Mining Access Patterns on Blocks

Jianwei Liao;François Trahay;Balazs Gerofi;Yutaka Ishikawa

IEEE Transactions on Parallel and Distributed Systems
Year: 2016 | Volume: 27, Issue: 9 | Journal Article |
Cited by: Papers (21)
Distributed file systems have been widely deployed as back-end storage systems to offer I/O services for parallel/distributed applications that process large amounts of data. Data prefetching in distributed file systems is a well-known optimization technique which can mask both network and disk latency and consequently boost I/O performance. Traditionally, data prefetching is initiated by the clie...Show More

Prefetching on Storage Servers through Mining Access Patterns on Blocks

Jianwei Liao;François Trahay;Balazs Gerofi;Yutaka Ishikawa

Year: 2016 | Volume: 27, Issue: 9 | Journal Article |

Interface for heterogeneous kernels: A framework to enable hybrid OS designs targeting high performance computing on manycore architectures

Taku Shimosawa;Balazs Gerofi;Masamichi Takagi;Gou Nakamura;Tomoki Shirasawa;Yuji Saeki;Masaaki Shimizu;Atsushi Hori;Yutaka Ishikawa

2014 21st International Conference on High Performance Computing (HiPC)
Year: 2014 | Conference Paper |
Cited by: Papers (26)
Turning towards exascale systems and beyond, it has been widely argued that the currently available systems software is not going to be feasible due to various requirements such as the ability to deal with heterogeneous architectures, the need for systems level optimization targeting specific applications, elimination of OS noise, and at the same time, compatibility with legacy applications. To co...Show More

Interface for heterogeneous kernels: A framework to enable hybrid OS designs targeting high performance computing on manycore architectures

Taku Shimosawa;Balazs Gerofi;Masamichi Takagi;Gou Nakamura;Tomoki Shirasawa;Yuji Saeki;Masaaki Shimizu;Atsushi Hori;Yutaka Ishikawa

Year: 2014 | Conference Paper |

Partially Separated Page Tables for Efficient Operating System Assisted Hierarchical Memory Management on Heterogeneous Architectures

Balazs Gerofi;Akio Shimada;Atsushi Hori;Yutaka Ishikawa

2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing
Year: 2013 | Conference Paper |
Heterogeneous architectures, where a multicore processor is accompanied with a large number of simpler, but more power-efficient CPU cores optimized for parallel workloads, are receiving a lot of attention recently. At present, these co-processors, such as the Intel Xeon Phi product family, come with limited on-board memory, which requires partitioning computational problems manually into pieces t...Show More

Partially Separated Page Tables for Efficient Operating System Assisted Hierarchical Memory Management on Heterogeneous Architectures

Balazs Gerofi;Akio Shimada;Atsushi Hori;Yutaka Ishikawa

Year: 2013 | Conference Paper |

Abstract: Toward Operating System Assisted Hierarchical Memory Management for Heterogeneous Architectures

Balazs Gerofi;Akio Shimada;Atsushi Hori;Yutaka Ishikawa

2012 SC Companion: High Performance Computing, Networking Storage and Analysis
Year: 2012 | Conference Paper |
Heterogeneous architectures, where a multicore processor is accompanied with a large number of simpler, but more power-efficient CPU cores optimized for parallel workloads, are receiving a lot of attention these days. Currently, these co-processors come with a limited on-board memory, which requires partitioning computational problems manually into pieces that can fit into the device's RAM as well...Show More

Abstract: Toward Operating System Assisted Hierarchical Memory Management for Heterogeneous Architectures

Balazs Gerofi;Akio Shimada;Atsushi Hori;Yutaka Ishikawa

Year: 2012 | Conference Paper |

Poster: Toward Operating System Assisted Hierarchical Memory Management for Heterogeneous Architectures

Balazs Gerofi;Akio Shimada;Atsushi Hori;Yutaka Ishikawa

2012 SC Companion: High Performance Computing, Networking Storage and Analysis
Year: 2012 | Conference Paper |
The Intel Many Integrated Core (Intel MIC) architecture is Intel's latest design targeted for processing highly parallel workloads. The Intel MIC architecture is implemented on a PCI card, and has its own on-board memory, connected to the host memory through PCI DMA operations. The on-board memory is faster than the one in the host, but it is significantly smaller, requiring the programmer to part...Show More

Poster: Toward Operating System Assisted Hierarchical Memory Management for Heterogeneous Architectures

Balazs Gerofi;Akio Shimada;Atsushi Hori;Yutaka Ishikawa

Year: 2012 | Conference Paper |

clone_n(): Parallel Thread Creation for Upcoming Many-Core Architectures

Balazs Gerofi;Atsushi Hori;Yutaka Ishikawa

2012 IEEE International Conference on Cluster Computing
Year: 2012 | Conference Paper |
Heterogeneous architectures, where a multicore processor, which is optimized for fast single-thread performance, is accompanied with a large number of simpler, but more power-efficient cores optimized for parallel workloads, such as NVIDIA's GPUs or Intel's Many Integrated Core (MIC), have been receiving a lot attention recently. Although NVIDIA's GPUs include built-in support for parallelism cont...Show More

clone_n(): Parallel Thread Creation for Upcoming Many-Core Architectures

Balazs Gerofi;Atsushi Hori;Yutaka Ishikawa

Year: 2012 | Conference Paper |

Workload Adaptive Checkpoint Scheduling of Virtual Machine Replication

Balazs Gerofi;Yutaka Ishikawa

2011 IEEE 17th Pacific Rim International Symposium on Dependable Computing
Year: 2011 | Conference Paper |
Cited by: Papers (3) | Patents (2)
Checkpoint-recovery based Virtual Machine (VM) replication is an emerging approach towards accommodating VM installations with high availability, especially, due to its inherent capability of tackling with symmetric multiprocessing (SMP) virtual machines, i.e. VMs with multiple virtual CPUs (vCPUs). However, it comes with the price of significant performance degradation of the application executed...Show More

Workload Adaptive Checkpoint Scheduling of Virtual Machine Replication

Balazs Gerofi;Yutaka Ishikawa

Year: 2011 | Conference Paper |

Utilizing Memory Content Similarity for Improving the Performance of Replicated Virtual Machines

Balazs Gerofi;Zoltan Vass;Yutaka Ishikawa

2011 Fourth IEEE International Conference on Utility and Cloud Computing
Year: 2011 | Conference Paper |
Checkpoint-recovery based Virtual Machine (VM) replication is an emerging approach towards accommodating VM installations with high availability. However, it comes with the price of significant performance degradation of the application executed in the VM due to the large amount of state that needs to be synchronized between the primary and the backup machines. It is therefore critical to find new...Show More

Utilizing Memory Content Similarity for Improving the Performance of Replicated Virtual Machines

Balazs Gerofi;Zoltan Vass;Yutaka Ishikawa

Year: 2011 | Conference Paper |

RDMA Based Replication of Multiprocessor Virtual Machines over High-Performance Interconnects

Balazs Gerofi;Yutaka Ishikawa

2011 IEEE International Conference on Cluster Computing
Year: 2011 | Conference Paper |
Cited by: Papers (5) | Patents (4)
With the growing prevalence of cloud computing and the increasing number of CPU cores in modern processors, symmetric multiprocessing (SMP) Virtual Machines (VM), i.e. virtual machines with multiple virtual CPUs, are gaining significance. However, accommodating SMP virtual machines with high availability at low overhead is still an open problem. Checkpoint-recovery based VM replication is an emerg...Show More

RDMA Based Replication of Multiprocessor Virtual Machines over High-Performance Interconnects

Balazs Gerofi;Yutaka Ishikawa

Year: 2011 | Conference Paper |

An Efficient Process Live Migration Mechanism for Load Balanced Distributed Virtual Environments

Balazs Gerofi;Hajime Fujita;Yutaka Ishikawa

2010 IEEE International Conference on Cluster Computing
Year: 2010 | Conference Paper |
Distributed virtual environments (DVE), such as multi-player online games and distributed simulations may involve a massive amount of concurrent clients. Deploying distributed server architectures is currently the most prevalent way of providing such large-scale services, where typically the virtual space is divided into several distinct regions requiring each server to handle only part of the vir...Show More

An Efficient Process Live Migration Mechanism for Load Balanced Distributed Virtual Environments

Balazs Gerofi;Hajime Fujita;Yutaka Ishikawa

Year: 2010 | Conference Paper |

A Multi-core Approach to Providing Fault Tolerance for Non-deterministic Services

Balazs Gerofi;Yutaka Ishikawa

2010 Ninth IEEE International Symposium on Network Computing and Applications
Year: 2010 | Conference Paper |
With the advent of multi- and many-core architectures, new opportunities in fault-tolerant computing have become available. In this paper we propose a novel process replication method that provides transparent failover of non-deterministic TCP services by utilizing spare CPU cores. Our method does not require any changes to the TCP protocol, does not require any changes to the client software, and...Show More

A Multi-core Approach to Providing Fault Tolerance for Non-deterministic Services

Balazs Gerofi;Yutaka Ishikawa

Year: 2010 | Conference Paper |

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