Tagged articles

149 articles

Page 1 of 2

May 13, 2026 · Industry Insights

Inside Huawei Atlas 900A3 SuperPoD (CM384) Supernode Wiring Scheme

The article provides a detailed technical analysis of Huawei's Atlas 900A3 SuperPoD (CM384) supernode wiring architecture, covering cabinet composition, NPU/CPU counts, 400 G optical interconnect design, cable installation practices, scalability to 24 Pods, and the performance benefits for AI workloads.

400G optical interconnectAtlas 900A3Data center wiring

0 likes · 8 min read

Inside Huawei Atlas 900A3 SuperPoD (CM384) Supernode Wiring Scheme

Machine Heart

May 11, 2026 · Artificial Intelligence

Can We Outsmart AI by Uploading Our Minds? MIT Dropout’s Plan for Digital Humans

Isaak Freeman, a former MIT PhD student, argues that humanity must embrace AI‑driven brain emulation—estimating that tens of thousands of H100 GPUs could simulate a human brain within a decade, but highlighting massive data‑acquisition, memory‑wall, and connectivity challenges that demand a multi‑decade, multi‑billion‑dollar effort.

AIHigh‑performance computingbrain emulation

0 likes · 8 min read

Can We Outsmart AI by Uploading Our Minds? MIT Dropout’s Plan for Digital Humans

Data Party THU

May 2, 2026 · Artificial Intelligence

Training an 11.5 B‑parameter Universal Interatomic Potential in Hours on Exascale Supercomputers

A Chinese Academy of Sciences team introduced the MatRIS‑MoE model and the Janus training framework, enabling a 11.5 billion‑parameter universal machine‑learning interatomic potential to be trained on two exascale systems at 1.2 EFLOPS, compressing weeks‑long training into a few hours.

AI for ScienceExascale trainingHigh‑performance computing

0 likes · 8 min read

Training an 11.5 B‑parameter Universal Interatomic Potential in Hours on Exascale Supercomputers

Deepin Linux

Mar 6, 2026 · Backend Development

Unlocking Ultra‑Low Latency: How RDMA Transforms High‑Performance Networking

This article explains the fundamentals of Remote Direct Memory Access (RDMA), its low‑latency, zero‑copy and kernel‑bypass mechanisms, programming interfaces, and real‑world applications in data‑center networks, high‑performance computing, and distributed storage, providing developers with practical guidance and code examples.

High‑performance computingLow latencyNetwork programming

0 likes · 31 min read

Unlocking Ultra‑Low Latency: How RDMA Transforms High‑Performance Networking

Alibaba Cloud Infrastructure

Dec 17, 2025 · Cloud Native

AI Training Revives Gang Scheduling in Kubernetes for Elastic Resource Orchestration

The article examines how the rise of large‑model AI training reintroduces the need for gang scheduling in Kubernetes, contrasting the rigid resource requirements of HPC‑style workloads with cloud‑native elasticity, and outlines the historical evolution, current implementations, and future directions for achieving more flexible, high‑throughput compute orchestration.

AI trainingCloud NativeGang Scheduling

0 likes · 22 min read

AI Training Revives Gang Scheduling in Kubernetes for Elastic Resource Orchestration

AntTech

Dec 4, 2025 · Artificial Intelligence

How AState Reduces Trillion‑Parameter RL Weight Sync to 6 Seconds

AState is a general‑purpose state data management system for reinforcement‑learning tasks that tackles low IO efficiency, slow weight synchronization, and state‑recovery challenges, achieving sub‑10‑second weight sync for trillion‑parameter models through a three‑layer architecture, zero‑redundancy transfers, and hardware‑aware co‑design, with the code openly available on GitHub.

AStateHigh‑performance computingWeight Synchronization

0 likes · 23 min read

How AState Reduces Trillion‑Parameter RL Weight Sync to 6 Seconds

AntTech

Nov 21, 2025 · Artificial Intelligence

How Awex Enables Sub‑Second TB‑Scale Weight Sync for Trillion‑Parameter RL Models

Awex is a high‑performance Python framework that synchronizes training and inference weights for trillion‑parameter reinforcement‑learning models in seconds, using unified conversion, metadata management, and NCCL/RDMA transfer plans, dramatically reducing RL training latency and supporting diverse parallel strategies.

Distributed TrainingHigh‑performance computingPython

0 likes · 17 min read

How Awex Enables Sub‑Second TB‑Scale Weight Sync for Trillion‑Parameter RL Models

AI Cyberspace

Nov 19, 2025 · Artificial Intelligence

Why MPI and NCCL Are Critical for Scaling AI Models Across Thousands of GPUs

This article explains how AI model training has evolved from single‑GPU workloads to massive distributed training using MPI for CPU‑centric communication and NCCL for GPU‑centric communication, covering their histories, core concepts, programming interfaces, topology discovery, protocol choices, and performance testing on multi‑GPU clusters.

AI distributed trainingGPU communicationHigh‑performance computing

0 likes · 71 min read

Why MPI and NCCL Are Critical for Scaling AI Models Across Thousands of GPUs

Kuaishou Tech

Nov 12, 2025 · Artificial Intelligence

How KaiFG Lets Python Feature Engineering Run at C++ Speed

KaiFG, Kuaishou's self‑built AI Feature Generator, unifies fragmented feature extraction frameworks, replaces slow C++ compilation cycles with Python‑level development, and achieves near‑C++ performance through Codon‑based compilation, reference‑counted memory management, and aggressive LLVM optimizations, dramatically shortening iteration time.

AI InfrastructureHigh‑performance computingfeature engineering

0 likes · 14 min read

How KaiFG Lets Python Feature Engineering Run at C++ Speed

Deepin Linux

Nov 11, 2025 · Fundamentals

Why RDMA Is the Secret to Lightning‑Fast Data Transfer in Modern Data Centers

This article explains the fundamentals of Remote Direct Memory Access (RDMA), its low‑latency, zero‑copy architecture, core principles, programming interfaces, and how it transforms data‑center networking, high‑performance computing, and distributed storage by bypassing the CPU and kernel.

High‑performance computingKernel BypassNetworking

0 likes · 30 min read