this paper explores useful resource allocation rules inside Wavelength Department Multiplexing (WDM) Optical Networks. WDM optical networks are leveraged to provide dependable and high-capability transmission for netw...
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Within the realm of natural language processing and computer vision, the synergy between Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) systems has emerged as some powerful paradigm for image ca...
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Summarization is a critical challenge in natural language processing, gaining prominence as individuals increasingly seek concise and accessible information. In the context of news articles, generating summaries from ...
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Approximate memories provide energy savings or performance improvements at the cost of occasional errors in stored data. Applications that tolerate errors on their data profit from this trade-off by controlling these ...
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ISBN:
(数字)9781665451550
ISBN:
(纸本)9781665451550
Approximate memories provide energy savings or performance improvements at the cost of occasional errors in stored data. Applications that tolerate errors on their data profit from this trade-off by controlling these errors to not affect critical data. this control usually involves programmer intervention with annotations in the source code. To avoid annotations, some techniques protect critical data that are common on many applications, isolating specific memory regions from errors. In this work, we propose and explore alternatives for the protection of application critical data by managing a supervisor execution environment with an approximate memory system. We expose only dynamically allocated data to errors with secure data manipulation through an approximate allocation scheme that divide stored data based on the approximation of the heap area. We evaluate 6 applications with different data access profiles and obtain up to 20% of energy savings.
Variational quantum algorithms (VQAs) can potentially solve practical problems using contemporary Noisy Intermediate Scale Quantum (NISQ) computers. VQAs find near-optimal solutions in the presence of qubit errors by ...
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ISBN:
(纸本)9798400700958
Variational quantum algorithms (VQAs) can potentially solve practical problems using contemporary Noisy Intermediate Scale Quantum (NISQ) computers. VQAs find near-optimal solutions in the presence of qubit errors by classically optimizing a loss function computed by parameterized quantum circuits. However, developing and testing VQAs is challenging due to the limited availability of quantum hardware, their high error rates, and the significant overhead of classical simulations. Furthermore, VQA researchers must pick the right initialization for circuit parameters, utilize suitable classical optimizer configurations, and deploy appropriate error mitigation methods. Unfortunately, these tasks are done in an adhoc manner today, as there are no software tools to configure and tune the VQA hyperparameters. In this paper, we present OSCAR (cOmpressed Sensing based Cost lAndscape Reconstruction) to help configure: 1) correct initialization, 2) noise mitigation techniques, and 3) classical optimizers to maximize the quality of the solution on NISQ hardware. OSCAR enables efficient debugging and performance tuning by providing users withthe loss function landscape without running thousands of quantum circuits as required by the grid search. Using OSCAR, we can accurately reconstruct the complete cost landscape with up to 100X speedup. Furthermore, OSCAR can compute an optimizer function query in an instant by interpolating a computed landscape, thus enabling the trial run of a VQA configuration with considerably reduced overhead.
Breast cancer poses a significant global health challenge, underscoring the need for effective diagnostic tools. this study introduces an automated breast cancer detection system utilizing deep learning techniques. By...
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the most used problem to compare these metaheuristics is the classical Travelling Salesman Problem (TSP) on a network with sizes from tens to millions of nodes. Nature-based metaheuristics are the main source of optim...
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ISBN:
(纸本)9798350381603
the most used problem to compare these metaheuristics is the classical Travelling Salesman Problem (TSP) on a network with sizes from tens to millions of nodes. Nature-based metaheuristics are the main source of optimization techniques to solve this problem. Although there is a zoo of possibilities, Ant Colony Optimization (ACO) is still one of the most efficient, parallel, and simple techniques to implement. the pheromone evaporation rate, alpha,beta, and ant number M are the four parameters fitted for finding the best solutions. Candidate list and the use of a source solution are efficient strategies to optimize large problems, but there are other strategies to improve quality solutions such as local search strategies. Among the local search techniques, k-opt search has proved to be very efficient to deal with path-crossing. the initial route is split into k sub-routes connected in (k- 1)!2(k-1) ways. thus, 3-opt is an efficient strategy balancing complexity and precision. Moreover, the best alpha and beta are the result of the used strategy. Finally, the solution is improved by accelerating through parallel versions with multiGPUs. In this article, four ACO algorithms were developed using two variations (Rank Based 3-opt and Strong Elitist 3-opt), each one with two strategies (candidate list and restricted). To test the algorithms, TSPLIB95 was used with test problems between N = 51 and 4, 461 nodes in a server with 4 RTX A4000 GPUs. After improving the algorithms and parallelization, the parameters are tuned to get the highest performance improving the local minimum search. Statistical analysis of repetitions shows good stability of the chosen metaheuristics.
this review paper provides an in-depth comprehensive overview of the application of Field-Programmable Gate Arrays(FGPAs) in battery energy storage systems (BESS) using solar cells. the integration of solar energy and...
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We address the efficient design and implementation of dense matrix factorizations and inversion (DMFI) on modern multicore processors with several NUMA (non-uniform memory access) nodes. Our approach enhances the DMFI...
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ISBN:
(数字)9781665451550
ISBN:
(纸本)9781665451550
We address the efficient design and implementation of dense matrix factorizations and inversion (DMFI) on modern multicore processors with several NUMA (non-uniform memory access) nodes. Our approach enhances the DMFI routines with a look-ahead strategy, in order to overcome the "panel factorization bottleneck". In addition, it exploits both hybrid task- and loop-level parallelizations while taking into account the NUMA organization of the memory hierarchy. the experiments on a Huawei Kunpeng-based server, with two sockets and 48 cores per socket, for three representative dense linear algebra operations, expose the necessity of adapting boththe codes and their execution environment parameters to improve data access locality. the results of these changes deliver performance across inter- and intra-socket NUMA configurations superior to that of reference implementations from state-of-the-art libraries for this platform.
In this paper, we present MSLIO, a code to mimic the I/O behavior of multiscale simulations. Such an I/O kernel is useful for HPC research, as it can be executed more easily and more efficiently than the full simulati...
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ISBN:
(数字)9781665451574
ISBN:
(纸本)9781665451574
In this paper, we present MSLIO, a code to mimic the I/O behavior of multiscale simulations. Such an I/O kernel is useful for HPC research, as it can be executed more easily and more efficiently than the full simulations when researchers are interested in the I/O load only. We validate MSLIO by comparing it to the I/O performance of an actual simulation, and we then use it to test some possible improvements to the output routine of the MHM (Multiscale Hybrid Mixed) library.
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