Nsei re plan optimisation
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This document discusses optimizing the network service entity (NSEI) planning for improved network performance. It shows that intra-PCU cell reselections provide better throughput and shorter service delays than inter-PCU reselections. The results of a new NSEI plan are presented, which balances the number of cells per PCU and ensures connected cells are on the same PCU. Information needed for NSEI re-planning is outlined.
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Hardware Acceleration of SVM Training for Real-time Embedded Systems: An Over...
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Heterogeneous computing with graphical processing unit: improvised back-prop...
A multitude of research has been rising for predicting the behavior of different real-world problems through machine learning models. An erratic nature occurs due to the augmented behavior and inadequacy of the prerequisite dataset for the prediction of water level over different fundamental models that show flat or low-set accuracy. In this paper, a powerful scaling strategy is proposed for improvised back-propagation algorithm using parallel computing for groundwater level prediction on graphical processing unit (GPU) for the Faridabad region, Haryana, India. This paper aims to propose the new streamlined form of a back-propagation algorithm for heterogeneous computing and to examine the coalescence of artificial neural network (ANN) with GPU for predicting the groundwater level. twenty years of data set from 2001-2020 has been taken into consideration for three input parameters namely, temperature, rainfall, and water level for predicting the groundwater level using parallelized backpropagation algorithm on compute unified device architecture (CUDA). This employs the back-propagation algorithm to be best suited to reinforce learning and performance by providing more accurate and fast results for water level predictions on GPUs as compared to sequential ones on central processing units (CPUs) alone.
Energy-efficient Query Processing in Web Search Engines
PESOS is an algorithm proposed to reduce the energy consumption of query processing nodes in web search engines while meeting latency requirements. It selects the appropriate CPU frequency on a per-core basis for each query based on query efficiency predictors that estimate processing volume and time. Results show PESOS can reduce CPU energy consumption by up to 48% compared to maximum frequency, and outperforms other approaches by 20% with less parameter tuning and fewer latency violations.
In datacenter performance analysis of a tensor processing unit
Tensorflow-KR 논문읽기모임 85번째 발표영상입니다 Google의 TPU v1논문에 대해서 발표했습니다(그런데 발표하고 하루만에 Google I/O에서 TPU v3가 발표됐네요... 논문이 나오길 기대합니다)
발표영상 : https://youtu.be/7WhWkhFAIO4
논문링크 : https://arxiv.org/abs/1704.04760
Tutorial-on-DNN-09A-Co-design-Sparsity.pdf
This document discusses various techniques for optimizing deep neural network models and hardware for efficiency. It covers approaches such as exploiting activation and weight statistics, sparsity, compression, pruning neurons and synapses, decomposing trained filters, and knowledge distillation. The goal is to reduce operations, memory usage, and energy consumption to enable efficient inference on hardware like mobile phones and accelerators. Evaluation methodologies are also presented to guide energy-aware design space exploration.
AVOIDING DUPLICATED COMPUTATION TO IMPROVE THE PERFORMANCE OF PFSP ON CUDA GPUS
The document discusses improving the performance of the Tabu Search algorithm for solving the Permutation Flowshop Scheduling Problem (PFSP) on CUDA GPUs by avoiding duplicated computation among threads. It first provides background on GPU architecture, the PFSP problem, and related parallelization methods. It then observes that if two permutations share the same prefix, their completion time tables will contain identical column data equal to the length of the prefix, leading to duplicated computation. The paper proposes an approach where each thread is assigned a permutation and allocated shared memory to store and compute the completion time table in parallel, avoiding this duplicated work by leveraging the shared prefix property. Experimental results show the new approach runs up to 1.5 times faster than an existing
AVOIDING DUPLICATED COMPUTATION TO IMPROVE THE PERFORMANCE OF PFSP ON CUDA GPUS
Graphics Processing Units (GPUs) have been emerged as powerful parallel compute platforms for various
application domains. A GPU consists of hundreds or even thousands processor cores and adopts Single
Instruction Multiple Threading (SIMT) architecture. Previously, we have proposed an approach that
optimizes the Tabu Search algorithm for solving the Permutation Flowshop Scheduling Problem (PFSP)
on a GPU by using a math function to generate all different permutations, avoiding the need of placing all
the permutations in the global memory. Based on the research result, this paper proposes another
approach that further improves the performance by avoiding duplicated computation among threads,
which is incurred when any two permutations have the same prefix. Experimental results show that the
GPU implementation of our proposed Tabu Search for PFSP runs up to 1.5 times faster than another GPU
implementation proposed by Czapiński and Barnes.
E035425030
This document summarizes a research paper that proposes a Virtual Backbone Scheduling technique with clustering and fuzzy logic for faster data collection in wireless sensor networks. It introduces the concepts of virtual backbone scheduling, clustering, and fuzzy logic. It presents the system architecture that uses these techniques and includes three clusters with sensor nodes, cluster heads, and a common sink node. Algorithms for virtual backbone scheduling and fuzzy-based clustering are described. Implementation results show that the proposed approach improves network lifetime, reduces error rates, lowers communication costs, and decreases scheduling time compared to existing techniques like TDMA scheduling.
Large-Scale Optimization Strategies for Typical HPC Workloads
Large-scale optimization strategies for typical HPC workloads include:
1) Building a powerful profiling tool to analyze application performance and identify bottlenecks like inefficient instructions, memory bandwidth, and network utilization.
2) Harnessing state-of-the-art hardware like new CPU architectures, instruction sets, and accelerators to maximize application performance.
3) Leveraging the latest algorithms and computational models that are better suited for large-scale parallelization and new hardware.
Cache Optimization Techniques for General Purpose Graphic Processing Units
This document summarizes research on adapting CPU cache optimization techniques for general purpose graphic processing units (GPGPUs). It first discusses related work on CPU and GPGPU cache architectures and optimization techniques. It then presents the conceptual design of selecting CPU techniques and analyzing their adaptation to GPGPUs. Two common CPU techniques, stride-one access and blocking, are adapted and experimental results show their effectiveness on a GPGPU, with blocking providing better performance than non-blocking approaches. The research contributes techniques for programmers to optimize GPGPU cache performance.
Low power network on chip architectures: A survey
Mostly communication now days is done through system on chip (SoC) models so, network on chip (NoC) architecture is most appropriate solution for better performance. However, one of major flaws in this architecture is power consumption. To gain high performance through this type of architecture it is necessary to confirm power consumption while designing this. Use of power should be diminished in every region of network chip architecture. Lasting power consumption can be lessened by reaching alterations in network routers and other devices used to form that network. This research mainly focusses on state-of-the-art methods for designing NoC architecture and techniques to reduce power consumption in those architectures like, network architecture, network links between nodes, network design, and routers.
Dominant block guided optimal cache size estimation to maximize ipc of embedd...
Embedded system software is highly constrained from performance, memory footprint, energy consumption and implementing cost view point. It is always desirable to obtain better Instructions per Cycle (IPC). Instruction cache has major contribution in improving IPC. Cache memories are realized on the same chip where the processor is running. This considerably increases the system cost as well. Hence, it is required to maintain a trade-off between cache sizes and performance improvement offered. Determining the number of cache lines and size of cache line are important parameters for cache designing. The design space for cache is quite large. It is time taking to execute the given application with different cache sizes on an instruction set simulator (ISS) to figure out the optimal cache size. In this paper, a technique is proposed to identify a number of cache lines and cache line size for the L1 instruction cache that will offer best or nearly best IPC. Cache size is derived, at a higher abstraction level, from basic block analysis in the Low Level Virtual Machine (LLVM) environment. The cache size estimated from the LLVM environment is cross validated by simulating the set of benchmark applications with different cache sizes in SimpleScalar’s out-of-order simulator. The proposed method seems to be superior in terms of estimation accuracy and/or estimation time as compared to the existing methods for estimation of optimal cache size parameters (cache line size, number of cache lines).
Dominant block guided optimal cache size estimation to maximize ipc of embedd...
Embedded system software is highly constrained from performance, memory footprint, energy consumption
and implementing cost view point. It is always desirable to obtain better Instructions per Cycle (IPC).
Instruction cache has major contribu
tion in improving IPC. Cache memories are realized on the same chip
where the processor is running. This considerably increases the system cost as well. Hence, it is required to
maintain a trade
-
off between cache sizes and performance improvement offered.
Determining the number
of cache lines and size of cache line are important parameters for cache designing. The design space for
cache is quite large. It is time taking to execute the given application with different cache sizes on an
instruction set simula
tor (ISS) to figure out the optimal cache size. In this paper, a technique is proposed to
identify a number of cache lines and cache line size for the L1 instruction cache that will offer best or
nearly best IPC. Cache size is derived, at a higher abstract
ion level, from basic block analysis in the Low
Level Virtual Machine (LLVM) environment. The cache size estimated from the LLVM environment is cross
validated by simulating the set of benchmark applications with different cache sizes in SimpleScalar’s out
-
of
-
order simulator. The proposed method seems to be superior in terms of estimation accuracy and/or
estimation time as compared to the existing methods for estimation of optimal cache size parameters (cache
line size, number of cache lines).
IEEE 2014 JAVA PARALLEL DISTRIBUTED PROJECTS Streaming applications on bus ba...
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To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Streaming applications on bus bas...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Streaming applications on bus bas...IEEEGLOBALSOFTSTUDENTSPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.orgSimilar to Nsei re plan optimisation (20)
EFFICIENT ABSOLUTE DIFFERENCE CIRCUIT FOR SAD COMPUTATION ON FPGA
EFFICIENT ABSOLUTE DIFFERENCE CIRCUIT FOR SAD COMPUTATION ON FPGA
EFFICIENT ABSOLUTE DIFFERENCE CIRCUIT FOR SAD COMPUTATION ON FPGA
EFFICIENT ABSOLUTE DIFFERENCE CIRCUIT FOR SAD COMPUTATION ON FPGA
EFFICIENT ABSOLUTE DIFFERENCE CIRCUIT FOR SAD COMPUTATION ON FPGA
EFFICIENT ABSOLUTE DIFFERENCE CIRCUIT FOR SAD COMPUTATION ON FPGA
IRJET- Flexible DSP Accelerator Architecture using Carry Lookahead Tree
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An air index for spatial query processing in road networks
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Hardware Acceleration of SVM Training for Real-time Embedded Systems: An Over...
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Energy-efficient Query Processing in Web Search Engines
Energy-efficient Query Processing in Web Search Engines
In datacenter performance analysis of a tensor processing unit
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AVOIDING DUPLICATED COMPUTATION TO IMPROVE THE PERFORMANCE OF PFSP ON CUDA GPUS
AVOIDING DUPLICATED COMPUTATION TO IMPROVE THE PERFORMANCE OF PFSP ON CUDA GPUS
AVOIDING DUPLICATED COMPUTATION TO IMPROVE THE PERFORMANCE OF PFSP ON CUDA GPUS
AVOIDING DUPLICATED COMPUTATION TO IMPROVE THE PERFORMANCE OF PFSP ON CUDA GPUS
Large-Scale Optimization Strategies for Typical HPC Workloads
Large-Scale Optimization Strategies for Typical HPC Workloads
Cache Optimization Techniques for General Purpose Graphic Processing Units
Cache Optimization Techniques for General Purpose Graphic Processing Units
Dominant block guided optimal cache size estimation to maximize ipc of embedd...
Dominant block guided optimal cache size estimation to maximize ipc of embedd...
Dominant block guided optimal cache size estimation to maximize ipc of embedd...
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IEEE 2014 JAVA PARALLEL DISTRIBUTED PROJECTS Streaming applications on bus ba...
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2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Streaming applications on bus bas...
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20240605 QFM017 Machine Intelligence Reading List May 2024
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Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
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Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
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UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
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3. Practical demonstrations
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UiPath integration with generative AI
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
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“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
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Removing Uninteresting Bytes in Software Fuzzing
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20240605 QFM017 Machine Intelligence Reading List May 2024
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Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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Nsei re plan optimisation
- 1. NSEI Re-Planning Rule and Optimisation Experience Shi Wei
- 3. Why need tuning NSEI ?
- 4. Time Series of Application Throughput during Cell Re-selection (Example) Each Cell Re-selection leads To a “Service Gap”
- 5. Comparison of Application Throughput and Service Delay for Intra and Inter-PCU Cell Re-Selections Intra-PCU cell re-selections provide much better performance than inter-PCU re-selections! Number of Samples Throughput for Intra -PCU Re-selection [kbits/sec] Throughput for Inter -PCU Re-selection [kbits/sec] Duration of Intra -PCU Service delay [sec] Duration of Inter -PCU Service delay [sec] 369 18.30 11.39 4.59 9.02
- 6. Comparison of Throughput during Cell Re-Selection for old and new PCU Plan During the drive survey the number of intra-PCU cell re-selections has increased significantly. This will bring performance benefits. PCU Plan Route Throughput for Intra -PCU Re-selection [kbits/sec] Throughput for Inter -PCU Re-selection [kbits/sec] Number Intra -PCU Re-selection Number Inter -PCU Re-selection Average Throughput for all Cell Re-selections [kbits/sec] PRE A 18.71 9.94 31 59 12.96 PRE B 17.34 10.84 33 70 12.92 POST A 18.34 13.2 61 31 16.61 POST B 18.81 11.59 58 26 16.58
- 7. TSL/PCU, GTRX/PCU, Cell/PCU Comparison un- balance
- 9. Need information for NSEI Re-Planning
- 11. Interaction between Tasks NSEI Planning Determining Number of GPRS TSLs Determine number Of PCUs Optimise Adjacency Plan Missing/Obsolete Ensure cells are on right BSC Only if all tasks are addressed accurately will the NSEI-plan be optimal
- 12. Results from NSEI Planning
- 15. MapView of the old&new NSEI-JinJiang
- 16. MapView of the old&new - SiShi
- 17. MapView of the old&new - NanAn
- 18. 市区 NSEI 调整后的观察验证 -1 © 2005 Nokia V1- Filename.ppt / yyyy-mm-dd / Initials
- 19. 市区 NSEI 调整后的观察验证 -2 © 2005 Nokia V1- Filename.ppt / yyyy-mm-dd / Initials
- 20. 市区 NSEI 调整后的观察验证 -3 NSEI 调整后路测效果图：
Editor's Notes
- 1 个 PCU 只有 16 个 EDAP