FIPIP: A novel fine-grained parallel partition based intra-frame prediction on heterogeneous many-core systems

Jiang, Wenbin; Long, Min; Yang, Laurence T.; Liu, Xiaobai; Jin, Hai; Yuille, Alan; Chi, Ye

doi:10.1016/j.future.2016.05.009

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…The 6th paper, authored by Wenbin Jiang et al [9], proposes a fine-grained parallelism for intra prediction based on GPU. The authors present a new regular prediction formula for parallelism and two optimized encoding orders are adopted for multi-levels parallelism.…”

Section: This Special Issuementioning

confidence: 99%

HPS-HDS: High Performance Scheduling for Heterogeneous Distributed Systems

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Iosup

Prodan

2018

Future Generation Computer Systems

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Heterogeneous Distributed Systems (HDS) are often characterized by a variety of resources that may or may not be coupled with specific platforms or environments. Such type of systems are Cluster Computing, Grid Computing, Peer-to-Peer Computing, Cloud Computing and Ubiquitous Computing all involving elements of heterogeneity, having a large variety of tools and software to manage them. As computing and data storage needs grow exponentially in HDS, increasing the size of data centers brings important diseconomies of scale. In this context, major solutions for scalability, mobility, reliability, fault tolerance and security are required to achieve high performance. More, HDS are highly dynamic in its structure, because the user requests must be respected as an agreement rule (SLA) and ensure QoS, so new algorithm for events and tasks scheduling and new methods for resource management should be designed to increase the performance of such systems. In this special issues, the accepted papers address the advance on scheduling algorithms, energy-aware models, self-organizing resource management, dataaware service allocation, Big Data management and processing, performance analysis and optimization.

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Section: This Special Issuementioning

confidence: 99%

HPS-HDS: High Performance Scheduling for Heterogeneous Distributed Systems

Pop

Iosup

Prodan

2018

Future Generation Computer Systems

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A Survey on Heterogeneous CPU–GPU Architectures and Simulators

Alaei,

Yazdanpanah

2024

Concurrency and Computation

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Heterogeneous architectures are vastly used in various high performance computing systems from IoT‐based embedded architectures to edge and cloud systems. Although heterogeneous architectures with cooperation of CPUs and GPUs and unified address space are increasingly used, there are still a lot of open questions and challenges regarding the design of these architectures. For evaluation, validation and exploration of next generation of heterogeneous CPU–GPU architectures, it is essential to use unified heterogeneous simulators for analyzing the execution of CPU–GPU workloads. This article presents a systematic review on challenges of heterogeneous CPU–GPU architectures with covering a diverse set of literatures on each challenge. The main considered challenges are shared resource management, network interconnections, task scheduling, energy consumption, and programming model. In addition, in this article, the state‐of‐the‐art of heterogeneous CPU–GPU simulation platforms is reviewed. The structure and characteristics of five cycle‐accurate heterogeneous CPU–GPU simulators are described and compared. We perform comprehensive discussions on the methodologies and challenges of designing high performance heterogeneous architectures. Moreover, for developing efficient heterogeneous CPU–GPU simulators, some recommendations are presented.

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FIPIP: A novel fine-grained parallel partition based intra-frame prediction on heterogeneous many-core systems

Cited by 2 publications

References 20 publications

HPS-HDS: High Performance Scheduling for Heterogeneous Distributed Systems

HPS-HDS: High Performance Scheduling for Heterogeneous Distributed Systems

A Survey on Heterogeneous CPU–GPU Architectures and Simulators

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