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30 result(s) for “ip can dealing site www canadiantire ca site can kernel org site:arxiv.org” of 71 matched · 1135 ms

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https://arxiv.org/pdf/2608.02122
) /Title (DEFT: Joint Task Placement and DVFS for Energy-Efficient Multi-GPU Runtimes) /Trapped /False /arXivID (https://arxiv

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https://arxiv.org/pdf/2407.00125
document was published isbn Text external ISBN for the publication in which the document was published doi Text external Digital Object Identifier for the document url URL external URL at which the document can be found byteCount Integer internal Approximate file size in octets pageCount Integer internal Number of

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https://arxiv.org/pdf/2608.01785
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Report GitHub Issue

https://arxiv.org/html/2608.02613v1
A benchmark for personal memory in this setting should therefore evaluate the full stack on device, not just whether a model can retrieve the relevant information

Mathematics

https://arxiv.org/list/math/new
Their finite iterates can develop rapidly increasing numbers of linear pieces, making them a natural test case for the expressive power of deep neural networks

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https://arxiv.org/src/2006.02759
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https://arxiv.org/pdf/2608.00785
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https://arxiv.org/src/2006.01048
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https://arxiv.org/pdf/2006.00342
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https://arxiv.org/pdf/2608.03118
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https://arxiv.org/src/2006.00064
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https://arxiv.org/pdf/2005.13905
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https://arxiv.org/src/2006.03720
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https://arxiv.org/src/2006.01128
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https://arxiv.org/src/2608.03883
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https://arxiv.org/src/2006.02085
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https://arxiv.org/pdf/2006.02602
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https://arxiv.org/src/2006.01331
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https://arxiv.org/pdf/2608.02666
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https://arxiv.org/src/2104.08691
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https://arxiv.org/pdf/2006.01128
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Report GitHub Issue

https://arxiv.org/html/2607.22511v2
A statement can be free of sorry and still be mathematically uninformative: a definition may reduce to True; or an unproven step may be introduced as an axiom that the kernel accepts

Condensed Matter

https://arxiv.org/list/cond-mat/new
The mixed anomaly between these is accommodated by the not-on-site action of the symmetries

[2608.12700] A Contract-Grade Verifier for LLM-Generated GPU Kernels

https://arxiv.org/abs/2608.12700
A kernel can pass that test and still be silently wrong

Stabilization of First-Order Partial Integro-Differential Equations with

https://arxiv.org/html/2608.02851v1
org perpetual non-exclusive license arXiv:2608

[2204.04218] Multimodal Multi-Head Convolutional Attention with Various

https://arxiv.org/abs/2204.04218
In many situations, computed tomography (CT) or magnetic resonance imaging (MRI) techniques capture several scans (modes) during a single investigation, which can jointly be used (in a multimodal fashion) to further boost the quality of super-resolution results

Rainfall is rough

https://arxiv.org/html/2607.27099v1
org perpetual non-exclusive license arXiv:2607

Report GitHub Issue

https://arxiv.org/html/2608.02612v1
org perpetual non-exclusive license arXiv:2608

Distributed, Parallel, and Cluster Computing

https://arxiv.org/list/cs.DC/new
SE) Automating GPU kernel optimization remains difficult in practice: generated variants can violate correctness constraints, runtime measurements are noisy, and search often stalls early

Databases

https://arxiv.org/list/cs.DB/new
We propose an architectural pattern for such systems, a trusted kernel with a generative shell, resting on one invariant: a component that can fabricate may influence which question the system answers, never which value it returns