作者: 愉快么 時(shí)間: 2025-3-21 22:32
https://doi.org/10.1007/978-3-642-71603-4pplications. Despite that, the process of correctly annotating complex applications is error-prone and may hinder the general adoption of these models. In this paper, we target the OmpSs-2 programming model and present a novel toolchain able to detect parallelization errors coming from non-compliant作者: degradation 時(shí)間: 2025-3-22 03:09
https://doi.org/10.1007/978-3-662-33233-7s, and machine learning executions require high performance computing platforms. Such infrastructures have been growing lately with the addition of thousands of newly designed components, calling their resiliency into question. It is crucial to solidify our knowledge on the way supercomputers fail. 作者: 閃光東本 時(shí)間: 2025-3-22 05:30
Die Gefahren der Vernetzung durch Vernetzungwhen a pipeline has reached its maximum performance capacity is generally a non-trivial task. Metrics exported at the software and at the hardware levels can provide insightful information about the current state of the system, but it can be difficult to interpret the value of a metric, or even to k作者: Acetaminophen 時(shí)間: 2025-3-22 10:28 作者: animated 時(shí)間: 2025-3-22 14:21 作者: animated 時(shí)間: 2025-3-22 20:32 作者: 使乳化 時(shí)間: 2025-3-22 23:08 作者: 名義上 時(shí)間: 2025-3-23 02:43 作者: 象形文字 時(shí)間: 2025-3-23 07:08
,Historikerstreit — transnational, intermediate layer outputs (called activations) computed during the forward phase must be stored until the corresponding gradient has been computed in the backward phase. These memory requirements sometimes prevent to consider larger batch sizes and deeper networks, so that they can limit both conv作者: Conflagration 時(shí)間: 2025-3-23 09:53 作者: 人類的發(fā)源 時(shí)間: 2025-3-23 15:18 作者: DEI 時(shí)間: 2025-3-23 18:11 作者: 抗原 時(shí)間: 2025-3-23 23:21 作者: 女歌星 時(shí)間: 2025-3-24 02:53 作者: 褲子 時(shí)間: 2025-3-24 09:41 作者: Offset 時(shí)間: 2025-3-24 12:46
Euro-Par 2020: Parallel Processing978-3-030-57675-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: SYN 時(shí)間: 2025-3-24 17:09
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/e/image/316544.jpg作者: 痛打 時(shí)間: 2025-3-24 22:40
,Historikerstreit — transnational,nd requires to determine which activations should be offloaded and when these transfers should take place. We prove that this problem is NP-complete in the strong sense, and propose two heuristics based on relaxations of the problem. We then conduct a thorough experimental evaluation of standard deep neural networks.作者: 征服 時(shí)間: 2025-3-25 02:12
https://doi.org/10.1007/978-3-642-91020-3g simulation. Among the results, it is shown that a XGRFC is able to connect 20k servers with 27% less routers than the corresponding XGFT and still providing the same performance under uniform traffic.作者: 木質(zhì) 時(shí)間: 2025-3-25 05:25
Optimal GPU-CPU Offloading Strategies for Deep Neural Network Trainingnd requires to determine which activations should be offloaded and when these transfers should take place. We prove that this problem is NP-complete in the strong sense, and propose two heuristics based on relaxations of the problem. We then conduct a thorough experimental evaluation of standard deep neural networks.作者: 下級(jí) 時(shí)間: 2025-3-25 10:20 作者: 任意 時(shí)間: 2025-3-25 12:20 作者: MIR 時(shí)間: 2025-3-25 19:53 作者: maverick 時(shí)間: 2025-3-25 22:04 作者: fender 時(shí)間: 2025-3-26 01:25 作者: 俗艷 時(shí)間: 2025-3-26 07:13
https://doi.org/10.1007/978-3-642-94213-6e the others are throttled. The overall execution performance is improved. Employing the . on diverse HPC benchmarks and real-world applications, we observed that the hardware settings adjusted by . have near-optimal results compared to the optimal setting of a static approach. The achieved speedup in our work amounts to up?to 6.3%.作者: 廚房里面 時(shí)間: 2025-3-26 09:46
Die Revision der Neurosenfrage,underlying parallel programming model and implemented our optimization framework in the LLVM toolchain. We evaluated it with ten benchmarks and obtained a geometric speedup of 2.3., and reduced on average 50% of the total bytes transferred between the host and GPU.作者: 迎合 時(shí)間: 2025-3-26 13:02
Marc Oliver Opresnik,Oguz Yilmazayers from state-of-the-art CNNs on two different GPU platforms, NVIDIA TITAN Xp and Tesla P4. The experiments show that the average speedup is 2.02 . on representative structures of CNNs, and 1.57. on end-to-end inference of SqueezeNet.作者: 圍裙 時(shí)間: 2025-3-26 20:31 作者: 玉米 時(shí)間: 2025-3-26 21:12 作者: 利用 時(shí)間: 2025-3-27 04:11
A Learning-Based Approach for Evaluating the Capacity of Data Processing Pipelineson accuracy when predicting on new configurations and when the number of data sources changes. Furthermore, our analysis demonstrates that the best prediction results are obtained when metrics of different types are combined.作者: 發(fā)微光 時(shí)間: 2025-3-27 08:44 作者: 星球的光亮度 時(shí)間: 2025-3-27 11:22
OmpMemOpt: Optimized Memory Movement for Heterogeneous Computingunderlying parallel programming model and implemented our optimization framework in the LLVM toolchain. We evaluated it with ten benchmarks and obtained a geometric speedup of 2.3., and reduced on average 50% of the total bytes transferred between the host and GPU.作者: AIL 時(shí)間: 2025-3-27 17:29
Accelerating Deep Learning Inference with Cross-Layer Data Reuse on GPUsayers from state-of-the-art CNNs on two different GPU platforms, NVIDIA TITAN Xp and Tesla P4. The experiments show that the average speedup is 2.02 . on representative structures of CNNs, and 1.57. on end-to-end inference of SqueezeNet.作者: staging 時(shí)間: 2025-3-27 18:39 作者: 不能仁慈 時(shí)間: 2025-3-27 21:59 作者: CODE 時(shí)間: 2025-3-28 03:32
Conference proceedings 2020nd, in August 2020. The conference was held virtually due to the coronavirus pandemic...The 39 full papers presented in this volume were carefully reviewed and selected from 158 submissions. They deal with parallel and distributed computing in general, focusing on support tools and environments; per作者: GIST 時(shí)間: 2025-3-28 08:18
Parallel Scheduling of Data-Intensive Tasksdress the problem of parallel scheduling of a DAG of data-intensive tasks to minimize makespan. To do so, we propose greedy online scheduling algorithms that take load balancing, data dependencies, and data locality into account. Simulations and an experimental evaluation using an Apache Spark cluster demonstrate the advantages of our solutions.作者: delegate 時(shí)間: 2025-3-28 10:55
0302-9743 nd distributed programming, interfaces, and languages; multicore and manycore parallelism; parallel numerical methods and applications; and accelerator computing..978-3-030-57674-5978-3-030-57675-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Outspoken 時(shí)間: 2025-3-28 16:46
Die Geb?ude der Universit?t Heidelberghe entire program, inside and outside loops. We first analyze the program statically and identify memory-access instructions that create data dependences that would appear in any execution of these instructions. Then, we exclude these instructions from instrumentation, allowing the profiler to skip 作者: Blood-Clot 時(shí)間: 2025-3-28 21:17
https://doi.org/10.1007/978-3-86226-355-4implementation yields lower overhead for lower threadcounts in some occasions. Neither implementation reacts to the system architecture, although the effects of the internal NUMA structure on the overhead can be observed.作者: confederacy 時(shí)間: 2025-3-29 02:24 作者: 繁榮地區(qū) 時(shí)間: 2025-3-29 05:36 作者: 密碼 時(shí)間: 2025-3-29 08:31
https://doi.org/10.1007/978-3-531-90404-7 to validate the newly introduced method, we perform extensive experiments on the . sparse direct solver. It demonstrates that our algorithm enables better static scheduling of the numerical factorization while keeping good data locality.作者: 連系 時(shí)間: 2025-3-29 11:33 作者: FRET 時(shí)間: 2025-3-29 15:40
A Comparison of the Scalability of OpenMP Implementationsimplementation yields lower overhead for lower threadcounts in some occasions. Neither implementation reacts to the system architecture, although the effects of the internal NUMA structure on the overhead can be observed.作者: 語(yǔ)言學(xué) 時(shí)間: 2025-3-29 20:14
Evaluating the Effectiveness of a Vector-Length-Agnostic Instruction Setble processors. Although the extent to which vector code is generated varies by mini-app, all compilers tested successfully utilise SVE to vectorise . code than they are able to when targeting NEON, Arm’s previous-generation SIMD instruction set. For most mini-apps, we expect performance improvement作者: GULLY 時(shí)間: 2025-3-30 03:11
A Makespan Lower Bound for the Tiled Cholesky Factorization Based on ALAP Scheduleze . on . processors. We show that this lower bound outperforms (is larger than) classical lower bounds from the literature. We also demonstrate that ALAP(.), an ALAP-based schedule where the number of resources is limited to ., has a makespan extremely close to the lower bound, thus establishing bo作者: amyloid 時(shí)間: 2025-3-30 04:40
Improving Mapping for Sparse Direct?Solvers to validate the newly introduced method, we perform extensive experiments on the . sparse direct solver. It demonstrates that our algorithm enables better static scheduling of the numerical factorization while keeping good data locality.作者: 可行 時(shí)間: 2025-3-30 11:35
Skipping Non-essential Instructions Makes Data-Dependence Profiling Fastersis, which may overestimate the number of dependences because it does not know many pointers values and array indices at compile time, profiling has the advantage of recording data dependences that actually occur at runtime. But it has the disadvantage of significantly slowing down program execution作者: Odyssey 時(shí)間: 2025-3-30 15:54 作者: exceed 時(shí)間: 2025-3-30 19:03
Towards a Model to Estimate the Reliability of Large-Scale Hybrid Supercomputerss, and machine learning executions require high performance computing platforms. Such infrastructures have been growing lately with the addition of thousands of newly designed components, calling their resiliency into question. It is crucial to solidify our knowledge on the way supercomputers fail. 作者: 記憶 時(shí)間: 2025-3-30 23:02 作者: 串通 時(shí)間: 2025-3-31 03:59
Operation-Aware Power Cappingr’s power consumption needs to be capped to avoid hardware damage. However, power capping often causes a computational performance loss because the underlying processors are clocked down. In this work, we developed an operation-aware management strategy, called ., to mitigate the performance loss. .作者: Obliterate 時(shí)間: 2025-3-31 06:42
A Comparison of the Scalability of OpenMP Implementationsormance at scale. Previous work has shown that overheads do not scale favourably in commonly used OpenMP implementations. Focusing on synchronization overhead, this work analyses the overhead of core OpenMP runtime library components for GNU and LLVM compilers, reflecting on the implementation’s sou
