Asynchronous Execution, Kernel Overlap, GPU Concurrency, Pipeline Parallelism

GeForce GTX 1650 SUPER prototype with 1152 CUDA cores and PCIe 4.0 interface surfaces
videocardz.com·1d
⏱️CUDA Events
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Looking for help with GTS random crashing
pastebin.com·6h·
Discuss: r/skyrimmods
📈GPU Occupancy
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Petri Dish Neural Cellular Automata
pub.sakana.ai·1d·
Discuss: Hacker News
🔗NCCL
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Needed a serial tool for high-frequency testing, so I built my own: CycBox
apps.microsoft.com·12h·
Discuss: r/embedded
📊Profiling Tools
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R²D²: Perception-Guided Task & Motion Planning for Long-Horizon Manipulation
developer.nvidia.com·3d
🏎️TensorRT
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High-speed and ultra-low-power superconductive neuron with ReLU activation
iopscience.iop.org·11h·
Discuss: Hacker News
Flash Attention
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Hybrid-Attention models are the future for SLMs
inference.net·2d·
Discuss: Hacker News
Flash Attention
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Connectivity Structure and Dynamics of Nonlinear Recurrent Neural Networks
journals.aps.org·2d
📉Model Quantization
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Intel "Panther Lake" Frequencies Leak, 16-Core SKU Hits 5.1 GHz
techpowerup.com·11h
🧠CPU Architecture
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Quantum-Resistant Federated Learning: Implementing Post-Quantum Cryptography for Secure Model Aggregation in Cross-Silo Envir...
dev.to·18h·
Discuss: DEV
🔄ONNX
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TCP vs UDP: Choosing the Right Protocol for Your Node.js Application
dev.to·1d·
Discuss: DEV
💡LSP
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Opportunistically Parallel Lambda Calculus
dl.acm.org·6d·
Discuss: Hacker News
💡LSP
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AI Safety at the Frontier: Paper Highlights of October 2025
lesswrong.com·13h
🤖AI Coding Tools
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Matrix Sensing with Kernel Optimal Loss: Robustness and Optimization Landscape
arxiv.org·22h
🔗Kernel Fusion
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Geometric Data Valuation via Leverage Scores
arxiv.org·22h
🔄ONNX
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Mastering Angular Signals: A Complete Guide Beyond RxJS
dev.to·16h·
Discuss: DEV
🦀PyO3
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Web Developer Travis McCracken on API Gateway Design with Rust and Go
dev.to·15h·
Discuss: DEV
🦀PyO3
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Formal Verification’s Value Grows
semiengineering.com·19h
⏱️Benchmarking
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Autobiasing Event Cameras for Flickering Mitigation
arxiv.org·22h
Flash Attention
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Demo: Statistically Significant Results On Biases and Errors of LLMs Do Not Guarantee Generalizable Results
arxiv.org·22h
🚀MLOps
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