Tootfinder

A Novel Deep Reinforcement Learning Method for Computation Offloading in Multi-User Mobile Edge Computing with Decentralization
Nguyen Chi Long, Trinh Van Chien, Ta Hai Tung, Van Son Nguyen, Trong-Minh Hoang, Nguyen Ngoc Hai Dang
https://arxiv.org/abs/2506.02458

A Novel Deep Reinforcement Learning Method for Computation Offloading in Multi-User Mobile Edge Computing with Decentralization
Mobile edge computing (MEC) allows appliances to offload workloads to neighboring MEC servers that have the potential for computation-intensive tasks with limited computational capabilities. This paper studied how deep reinforcement learning (DRL) algorithms are used in an MEC system to find feasible decentralized dynamic computation offloading strategies, which leads to the construction of an extensible MEC system that operates effectively with finite feedback. Even though the Deep Determinist…

DiOMP-Offloading: Toward Portable Distributed Heterogeneous OpenMP
Baodi Shan, Mauricio Arayr-Polo, Barbara Chapman
https://arxiv.org/abs/2506.02486 https:…

DiOMP-Offloading: Toward Portable Distributed Heterogeneous OpenMP
As core counts and heterogeneity rise in HPC, traditional hybrid programming models face challenges in managing distributed GPU memory and ensuring portability. This paper presents DiOMP, a distributed OpenMP framework that unifies OpenMP target offloading with the Partitioned Global Address Space (PGAS) model. Built atop LLVM/OpenMP and using GASNet-EX or GPI-2 for communication, DiOMP transparently handles global memory, supporting both symmetric and asymmetric GPU allocations. It leverages O…

🇺🇦 #NowPlaying on KEXP's #MiddayShow
Adamski:
🎵 Killer (Radio Edit)
#Adamski
https://opaledit.bandcamp.com/track/adamski-killer-opal-remix
https://open.spotify.com/track/5jhDeobWatA1XAHWKOWPh2

Improving compiler support for SIMD offload using Arm Streaming SVE
Mohamed Husain Noor Mohamed, Adarsh Patil, Latchesar Ionkov, Eric Van Hensbergen
https://arxiv.org/abs/2506.02233

Improving compiler support for SIMD offload using Arm Streaming SVE
The wider adoption of tightly coupled core-adjacent accelerators, such as Arm Scalable Matrix Extension (SME), hinges on lowering software programming complexity. In this paper, we focus on enabling the use of SME architecture in Streaming Scalable Vector Extension (SSVE) mode for workloads written in C/C++. While current compilers optimize loops for all types of SIMD instructions, these techniques primarily target vector units within the core and falter when applied to disaggregated, core-adja…

🇺🇦 Auf radioeins läuft...
Electric Light Orchestra (ELO):
🎵 Evil Woman
#NowPlaying #ElectricLightOrchestraELO
https://opaledit.bandcamp.com/track/electric-light-orchestra-evil-woman-opal-gta-edit
https://open.spotify.com/track/4N6HYihypXOVL24bnaR4fp

Några tänkvärda poänger av Guillou, särskilt det där med ammunitionen. https://www.aftonbladet.se/nyheter/kolumnister/a/EyrW8G/polisen-i-orebro-ville-ha-ett-opolitiskt-massmord

Polisen i Örebro ville ha ett opolitiskt massmord
Utredningen av Rickard Anderssons, 35, massmord på Campus Risbergska i Örebro är klar. Den kan därmed läggas till handlingarna och glömskan med polisens diffusa

#PennedPossibilities 690 — SC POV: What do you fear losing the most?
This question is for the MC's brother, I believe.
SC: My brother.
It terrifies me to think that my brother could become completely corrupted and lose his true Ethereal essence to Ianthe's manipulation. You see, The Unmaking is not just simple indoctrination, it is the obliteration of free will,…

Gravitational Redshift for Rapidly Rotating Neutron Stars
Zsombor Jakab, Sharon Morsink
https://arxiv.org/abs/2507.02234 https://arxi…

Gravitational Redshift for Rapidly Rotating Neutron Stars
The Oblate Schwarzschild (OS) approximation is a method often used to compute the flux of X-rays emitted from a rapidly rotating neutron star. In this approximation, the oblate shape of the rotating star is embedded in the Schwarzschild metric, which is used to compute the redshift of photon energies as they propagate from the star to the telescope. In this paper, we demonstrate that there are small errors introduced by the standard treatment of photon redshift in the OS approximation and provi…

Extremely large oblate deformation of the first excited state in $^{12}$C: a new challenge to modern nuclear theory
C. Ngwetsheni, J. N. Orce, P. Navr\'atil, P. E. Garrett, T. Faestermann, A. Bergmaier, M. Frosini, V. Bildstein, B. A. Brown, C. Burbadge, T. Duguet, K. Hady\'nska-Kl\c{e}k, M. Mahgoub, C. V. Mehl, A. Pastore, A. Radich, S. Triambak

Extremely large oblate deformation of the first excited state in $^{12}$C: a new challenge to modern nuclear theory
A Coulomb-excitation study of the high-lying first excited state at 4.439 MeV in the nucleus $^{12}$C has been carried out using the $^{208}$Pb($^{12}$C,$^{12}$C$^*$)$^{208}$Pb$^*$ reaction at 56 MeV and the {\sc Q3D} magnetic spectrograph at the Maier-Leibnitz Laboratorium in Munich. High-statistics achieved with an average beam intensity of approximately 10$^{11}$ ions/s together with state-of-the-art {\it ab initio} calculations of the nuclear dipole polarizability permitted the accurate det…

Parallel CPU-GPU Execution for LLM Inference on Constrained GPUs
Jiakun Fan, Yanglin Zhang, Xiangchen Li, Dimitrios S. Nikolopoulos
https://arxiv.org/abs/2506.03296

Parallel CPU-GPU Execution for LLM Inference on Constrained GPUs
Deploying large language models (LLMs) for online inference is often constrained by limited GPU memory, particularly due to the growing KV cache during auto-regressive decoding. Hybrid GPU-CPU execution has emerged as a promising solution by offloading KV cache management and parts of attention computation to the CPU. However, a key bottleneck remains: existing schedulers fail to effectively overlap CPU-offloaded tasks with GPU execution during the latency-critical, bandwidth-bound decode phase…