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Model Perspective
Model Perspective
Apr 19, 2026 · Industry Insights

How Humanoid Robots Slashed Marathon Times from 2h40 to 50 min – A Mathematical Analysis

The article models the dramatic performance jump of Chinese humanoid robots in half‑marathon races, explains the exponential decay curve built from two data points, examines cooling, weight reduction, battery, and navigation breakthroughs, and discusses the broader industry implications and limits of this rapid progress.

AIHumanoid RobotsPerformance Modeling
0 likes · 9 min read
How Humanoid Robots Slashed Marathon Times from 2h40 to 50 min – A Mathematical Analysis
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 6, 2026 · Artificial Intelligence

How Tair‑KVCache‑HiSim Simulates LLM Inference 390 000× Faster with <5% Error

This article explains the design, challenges, and high‑fidelity architecture of Tair‑KVCache‑HiSim, a simulation tool that models multi‑level KV‑Cache behavior for large‑language‑model inference, predicts latency, throughput and cost under SLO constraints, and validates its predictions against real GPU deployments with sub‑5% error.

AI InfrastructureCost OptimizationKVCache
0 likes · 32 min read
How Tair‑KVCache‑HiSim Simulates LLM Inference 390 000× Faster with <5% Error
Infra Learning Club
Infra Learning Club
Mar 6, 2025 · Fundamentals

How GPU DVFS Boosts Efficiency: Concepts, Modeling, and Future Directions

This article explains how GPU Dynamic Voltage and Frequency Scaling (DVFS) reduces power consumption while preserving performance, describes NVIDIA GPU Boost 4.0 features, outlines a hardware‑counter‑based GPGPU power‑estimation model built with a BP‑ANN, reports sub‑5% error on benchmarks, and discusses intelligent and multi‑GPU extensions.

BP-ANNDVFSGPGPU
0 likes · 5 min read
How GPU DVFS Boosts Efficiency: Concepts, Modeling, and Future Directions
Kuaishou Large Model
Kuaishou Large Model
Nov 22, 2024 · Artificial Intelligence

Boost LLM Training on Massive Clusters with DP/TP Overlap and Context Parallelism

This article details a comprehensive set of techniques—including data‑ and tensor‑parallel overlap, context‑parallelism, activation rematerialization, and a performance‑driven cost model—that dramatically improve large‑language‑model training efficiency on ultra‑large GPU clusters while preserving model quality.

Distributed TrainingParallelismPerformance Modeling
0 likes · 28 min read
Boost LLM Training on Massive Clusters with DP/TP Overlap and Context Parallelism
Kuaishou Tech
Kuaishou Tech
Nov 21, 2024 · Artificial Intelligence

Best Practices for Training Large Language Models on Ultra‑Large Scale Clusters

This article summarizes the challenges of distributed training for massive language models and presents a suite of solutions—including DP/TP/PP overlap, context parallelism, efficient recomputation, and a performance‑aware cost model—that together boost training throughput by over 30% on large GPU clusters.

Distributed TrainingGPU clustersPerformance Modeling
0 likes · 27 min read
Best Practices for Training Large Language Models on Ultra‑Large Scale Clusters
Kuaishou Large Model
Kuaishou Large Model
Jul 11, 2024 · Artificial Intelligence

Pipeline-Aware Offloading & Balanced Checkpointing Accelerate LLM Training

Researchers from Kwai’s large-model team present a novel training system that combines pipeline-parallel-aware activation offloading with a compute-memory balanced checkpointing strategy, enabling lossless acceleration of large language models, achieving up to 42.7% MFU on 256 NVIDIA H800 GPUs while reducing memory usage.

GPU trainingKwaiPerformance Modeling
0 likes · 13 min read
Pipeline-Aware Offloading & Balanced Checkpointing Accelerate LLM Training
OPPO Kernel Craftsman
OPPO Kernel Craftsman
Aug 19, 2022 · Fundamentals

Superscalar Processor Architecture and Performance Modeling for Mobile Devices

Modern mobile CPUs are superscalar, using deep pipelining, branch prediction, register renaming, out‑of‑order issue, execution, write‑back, and commit stages to boost instruction‑level parallelism, while performance modeling via CPI and hardware counters helps engineers overcome power, memory, and compiler limitations for efficient code.

CPUMobile ProcessorPerformance Modeling
0 likes · 13 min read
Superscalar Processor Architecture and Performance Modeling for Mobile Devices
IT Architects Alliance
IT Architects Alliance
Mar 5, 2022 · Operations

High Availability Overview and Design for Business Systems

This article explains the concepts, metrics, planning stages, and architectural components of high availability for business systems, covering reliability, performance, scalability, evaluation phases, performance modeling, and practical implementation guidelines to achieve four‑nine (99.99%) uptime.

Non-functional RequirementsPerformance ModelingSystem Architecture
0 likes · 17 min read
High Availability Overview and Design for Business Systems
Alibaba Cloud Native
Alibaba Cloud Native
Dec 14, 2021 · Cloud Native

How CPU Burst Improves Container Performance Without Reducing Deployment Density

This article explains the CPU Burst feature added in Linux 5.14, how it mitigates fine‑grained CPU throttling in Kubernetes containers, presents a queue‑theoretic model and Monte‑Carlo simulations to evaluate its impact on scheduler stability, and offers practical guidance for safely enabling it in production environments.

CPU BurstCloud NativeKubernetes
0 likes · 14 min read
How CPU Burst Improves Container Performance Without Reducing Deployment Density
IT Architects Alliance
IT Architects Alliance
Oct 24, 2021 · Databases

Database Capacity Planning and Scaling with ScyllaDB

This article explains why database capacity planning is challenging and presents a systematic approach—including workload analysis, performance modeling, consistency considerations, and node scaling decisions—using the open‑source NoSQL database ScyllaDB to guide accurate capacity estimation.

ConsistencyNoSQLPerformance Modeling
0 likes · 14 min read
Database Capacity Planning and Scaling with ScyllaDB
21CTO
21CTO
Oct 21, 2021 · Databases

Why Is Database Capacity Planning So Hard? Simplify with ScyllaDB

This article explains why sizing a database cluster is challenging, outlines a step‑by‑step methodology for estimating workload, configuration and performance, discusses the impact of consistency levels, secondary indexes, materialized views and maintenance, and shows how ScyllaDB can be used to model and simplify capacity planning.

ConsistencyDatabase CapacityNoSQL
0 likes · 16 min read
Why Is Database Capacity Planning So Hard? Simplify with ScyllaDB
ITPUB
ITPUB
Oct 20, 2021 · Databases

Why Is Database Capacity Planning So Hard? A Practical Guide Using ScyllaDB

This article explains why sizing a database cluster is challenging, outlines a systematic capacity‑planning process, examines workload characteristics, query‑operation mapping, consistency trade‑offs, and maintenance considerations, and demonstrates how the open‑source NoSQL database ScyllaDB can be used to model and simplify these decisions.

NoSQLPerformance ModelingScyllaDB
0 likes · 15 min read
Why Is Database Capacity Planning So Hard? A Practical Guide Using ScyllaDB
Architects' Tech Alliance
Architects' Tech Alliance
Feb 17, 2019 · Operations

Modeling SSD Garbage Collection as a Gambler's Ruin Problem: Probabilistic Analysis and Control Strategies

By drawing analogies between casino gambling and SSD garbage collection, the article uses probability theory, Brownian motion, and stochastic processes to model victim block selection, resource depletion, and I/O bandwidth fluctuations, proposing control strategies that balance performance stability and resource safety.

Garbage CollectionPerformance ModelingSSD
0 likes · 21 min read
Modeling SSD Garbage Collection as a Gambler's Ruin Problem: Probabilistic Analysis and Control Strategies