Parallel Computing - Edgentiq

Random Batch Attention: A New Approach to Efficient Graph Transformers

Optimizing AI Inference: A 3D Approach to Balancing Performance, Cost, and Speed

Planned Diffusion: A Hybrid Method for Efficient LLM Text Generation

Guiding AI to Optimize CUDA Code: A Deep Dive into LLM Tutoring for Parallel Programming

Accelerating Optimization: A Parallel Approach to the Artificial Protozoa Optimizer

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Faster AI Agents: A Framework for Parallel Execution

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Boosting HPC Performance with VibeCodeHPC’s Multi-Agent AI

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The Dual Strengths of AI Reasoning: Chain-of-Thought for Approximation, Latent Thought for Parallelism

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TransZero: Accelerating AI Planning with Parallel Tree Expansion

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ParaThinker: Unlocking LLM Reasoning Potential Through Native Parallel Thinking

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AI Adviser Aira Boosts Performance for Time-Sensitive Software on SMT Processors

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E-boost: A New Approach to Efficient E-Graph Optimization

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Unpacking Energy Use in Parallel Neural Network Training

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PARBALANS: Advancing Mixed-Integer Programming with Parallel Adaptive Search

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Automating OpenMP Scheduling: A Look at Expert and AI Approaches

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Boosting Constraint Solving: How GPUs Accelerate Table Propagation

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Advancing Genomic Data Compression with Parallel Multi-Knowledge Learning

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Intra-DP: Overlapping Computation and Communication for High-Performance AI at the Mobile Edge

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Unlocking Global Optimization: A New GPU-Powered Search Method for Complex Functions

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Gen AI News and Updates

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Random Batch Attention: A New Approach to Efficient Graph Transformers

November 11, 2025

Optimizing AI Inference: A 3D Approach to Balancing Performance, Cost, and Speed

October 23, 2025

Planned Diffusion: A Hybrid Method for Efficient LLM Text Generation

October 22, 2025