# Data Analytics / Processing

Published articles for Data Analytics / Processing.

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## Scaling Federated Learning Across Docker, Kubernetes, and Slurm with NVIDIA FLARE

DevFeed: [Scaling Federated Learning Across Docker, Kubernetes, and Slurm with NVIDIA FLARE](<https://devfeed.tech/articles/scaling-federated-learning-across-docker-kubernetes-and-slurm-with-nvidia-flare-26915.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/scaling-federated-learning-across-docker-kubernetes-and-slurm-with-nvidia-flare/>)

Author: Elizabeth Goodman

Published: 2026-09-15T15:00:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Federated Learning](<https://devfeed.tech/topics/federated-learning.md>), [Docker](<https://devfeed.tech/topics/docker.md>), [Kubernetes](<https://devfeed.tech/topics/kubernetes.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Server](<https://devfeed.tech/topics/server.md>), [dataset](<https://devfeed.tech/topics/dataset.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>)

Tags: [architecture](<https://devfeed.tech/tags/architecture.md>), [compute](<https://devfeed.tech/tags/compute.md>), [configuration](<https://devfeed.tech/tags/configuration.md>), [container](<https://devfeed.tech/tags/container.md>), [data-analytics-processing](<https://devfeed.tech/tags/data-analytics-processing.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [docker](<https://devfeed.tech/tags/docker.md>), [docker-container](<https://devfeed.tech/tags/docker-container.md>), [federated-learning](<https://devfeed.tech/tags/federated-learning.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [job](<https://devfeed.tech/tags/job.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-flare](<https://devfeed.tech/tags/nvidia-flare.md>), [server](<https://devfeed.tech/tags/server.md>)

### AI overview

This article explains how NVIDIA FLARE scales federated learning across sites with different infrastructure, including Docker, Kubernetes, and Slurm. Its two-layer architecture separates persistent federation services from on-demand job execution, while allowing each site to retain local control over compute, data, secrets, and scheduling.

### Source excerpt

Federated learning (FL) projects often begin with a straightforward setup: one server, a few clients, and one dataset at each site. As those projects grow, the...

## GPU-Accelerated Clustering for Financial Instruments at Scale

DevFeed: [GPU-Accelerated Clustering for Financial Instruments at Scale](<https://devfeed.tech/articles/gpu-accelerated-clustering-for-financial-instruments-at-scale-6832.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/gpu-accelerated-clustering-for-financial-instruments-at-scale/>)

Author: Elizabeth Goodman

Published: 2026-08-21T16:21:04Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [GPU](<https://devfeed.tech/topics/gpu.md>), [Algorithm](<https://devfeed.tech/topics/algorithm.md>), [Matrix](<https://devfeed.tech/topics/matrix-org.md>)

Tags: [batch](<https://devfeed.tech/tags/batch.md>), [communication](<https://devfeed.tech/tags/communication.md>), [data-analytics-processing](<https://devfeed.tech/tags/data-analytics-processing.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [financial-services](<https://devfeed.tech/tags/financial-services.md>), [gb200](<https://devfeed.tech/tags/gb200.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [memory](<https://devfeed.tech/tags/memory.md>), [nemo](<https://devfeed.tech/tags/nemo.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [post](<https://devfeed.tech/tags/post.md>), [scale](<https://devfeed.tech/tags/scale.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>), [workflow](<https://devfeed.tech/tags/workflow.md>)

### AI overview

A GPU-accelerated workflow uses rolling correlation and tail-dependence matrices to cluster financial instruments for portfolio construction, risk aggregation, statistical arbitrage, and trade surveillance. Its adaptive SymNMF-based solver supports soft factor loadings and hard cluster labels, while memory-efficient and distributed implementations scale from single GPUs to one million instruments across multiple nodes.

### Source excerpt

Use AdaptGrow, a GPU-accelerated matrix factorization algorithm, to turn rolling correlation and tail-dependence matrices into hard clusters, soft factor...

## Run Massive-Scale UMAP in Minutes Using Multiple GPUs--Without Losing Accuracy

DevFeed: [Run Massive-Scale UMAP in Minutes Using Multiple GPUs--Without Losing Accuracy](<https://devfeed.tech/articles/run-massive-scale-umap-in-minutes-using-multiple-gpus-without-losing-accuracy-6933.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/run-massive-scale-umap-in-minutes-using-multiple-gpus-without-losing-accuracy/>)

Author: Tanya Lenz

Published: 2026-08-18T16:48:08Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Data analysis](<https://devfeed.tech/topics/data-analysis.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Graphs](<https://devfeed.tech/topics/graphs.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [RAPIDS](<https://devfeed.tech/topics/rapids.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>)

Tags: [accelerate](<https://devfeed.tech/tags/accelerate.md>), [analysis](<https://devfeed.tech/tags/analysis.md>), [cuda-x](<https://devfeed.tech/tags/cuda-x.md>), [data-analytics-processing](<https://devfeed.tech/tags/data-analytics-processing.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [feature](<https://devfeed.tech/tags/feature.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [graphs](<https://devfeed.tech/tags/graphs.md>), [multi-gpu](<https://devfeed.tech/tags/multi-gpu.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [post](<https://devfeed.tech/tags/post.md>), [scale](<https://devfeed.tech/tags/scale.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>), [training](<https://devfeed.tech/tags/training.md>), [vector](<https://devfeed.tech/tags/vector.md>), [visualization](<https://devfeed.tech/tags/visualization.md>)

### AI overview

This article explains how multi-GPU UMAP scales dimensionality reduction to datasets containing tens to hundreds of millions of vectors. A feature in NVIDIA cuML and cuVS 25.06 distributes all-neighbors kNN graph construction across multiple GPUs, enabling workloads of several hundred gigabytes to run in minutes while preserving nearest-neighbor relationships and accuracy.

### Source excerpt

Uniform Manifold Approximation and Projection (UMAP) is a dimensionality reduction technique widely used for visualization and feature extraction. Applications...

## Designing GPU-Accelerated Query Engines with NVIDIA GQE

DevFeed: [Designing GPU-Accelerated Query Engines with NVIDIA GQE](<https://devfeed.tech/articles/designing-gpu-accelerated-query-engines-with-nvidia-gqe-6799.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/designing-gpu-accelerated-query-engines-with-nvidia-gqe/>)

Author: Michelle Horton

Published: 2026-06-30T17:36:43Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [GPU](<https://devfeed.tech/topics/gpu.md>), [Databases](<https://devfeed.tech/topics/databases.md>), [NCCL](<https://devfeed.tech/topics/nccl.md>), [CUDA](<https://devfeed.tech/topics/cuda.md>), [NVLink](<https://devfeed.tech/topics/nvlink.md>), [Compression](<https://devfeed.tech/topics/compression.md>), [IO](<https://devfeed.tech/topics/io.md>), [SQL](<https://devfeed.tech/topics/sql.md>), [Parser](<https://devfeed.tech/topics/parser.md>), [Streaming](<https://devfeed.tech/topics/streaming.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [accelerate](<https://devfeed.tech/tags/accelerate.md>), [compression](<https://devfeed.tech/tags/compression.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [cuda-x](<https://devfeed.tech/tags/cuda-x.md>), [data](<https://devfeed.tech/tags/data.md>), [data-analytics-processing](<https://devfeed.tech/tags/data-analytics-processing.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [databases](<https://devfeed.tech/tags/databases.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [memory](<https://devfeed.tech/tags/memory.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvlink](<https://devfeed.tech/tags/nvlink.md>), [performance](<https://devfeed.tech/tags/performance.md>), [sql](<https://devfeed.tech/tags/sql.md>)

### AI overview

This article presents GQE, a reference architecture for executing SQL queries on GPUs. It explains how NVIDIA hardware and CUDA-X libraries address memory, I/O, data movement, decompression, and end-to-end performance challenges for large datasets.

### Source excerpt

GPU-accelerated query engines are often constrained by memory and I/O bandwidth. NVIDIA hardware advances--including high bandwidth memory (HBM), NVIDIA...