# packing Published articles for packing. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Introducing advanced Kubernetes control plane configuration in Amazon EKS DevFeed: [Introducing advanced Kubernetes control plane configuration in Amazon EKS]() Original publisher: [Read original article]() Author: Ashok Srirama Published: 2026-08-12T17:23:45Z Content type: article Language: en Sources: [Containers]() Topics: [Amazon EKS](), [Kubernetes](), [configuration](), [API](), [Availability](), [Provisioning]() Tags: [advanced-300](), [ai-ml](), [amazon-eks](), [amazon-elastic-kubernetes-service](), [announcements](), [api](), [availability](), [ci-cd](), [kubernetes](), [packing](), [retention](), [storage]() ### AI overview Amazon EKS introduces advanced Kubernetes control plane configuration, allowing users to set API server, scheduler, and controller manager parameters directly. The article covers pod placement scoring, event retention, and HPA synchronization settings through feature explanations and hands-on walkthroughs. ### Source excerpt With Amazon EKS, you can now configure Kubernetes control plane components (the API server, scheduler, and controller manager) directly through EKS APIs. This post explains what's configurable and includes two hands-on walkthroughs: enabling MostAllocated bin-packing to optimize pod placement, and tuning event retention duration. ## Solving virtual machine puzzles: How AI is optimizing cloud computing DevFeed: [Solving virtual machine puzzles: How AI is optimizing cloud computing]() Original publisher: [Read original article]() Published: 2025-10-17T17:56:35Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [cloud-computing](), [Algorithms & Theory](), [data centers](), [servers](), [Provisioning]() Tags: [2025](), [ai](), [algorithms](), [algorithms-theory](), [cloud](), [cloud-computing](), [distributed-systems-parallel-computing](), [efficiency](), [google](), [packing](), [provisioning](), [research](), [resource](), [scale](), [server](), [servers](), [software-systems-engineering](), [virtual-machines]() ### AI overview Google Research presents LAVA, a scheduling system that uses AI models to continuously predict and adapt to virtual machine lifetimes. Its NILAS, LAVA, and LARS algorithms improve VM allocation and rescheduling in large cloud data centers, reducing stranded resources and improving server efficiency. ### Source excerpt Algorithms & Theory ## Packing Matrix-Vector Multiplication in Fully Homomorphic Encryption DevFeed: [Packing Matrix-Vector Multiplication in Fully Homomorphic Encryption]() Original publisher: [Read original article]() Published: 2024-09-07T04:18:09Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [homomorphic encryption](), [FHE](), [Encryption](), [layout](), [parallel](), [Python]() Tags: [arithmetic](), [code](), [cryptography](), [data](), [encryption](), [fhe](), [github-repository](), [homomorphic-encryption](), [layout](), [linear-algebra](), [lwe](), [mathematics](), [packing](), [parallel](), [programming](), [python](), [rlwe](), [simd](), [strategies]() ### AI overview This article explains packing for SIMD-style fully homomorphic encryption. It describes how to arrange plaintext data in RLWE ciphertexts so matrix-vector multiplication requires fewer alignment multiplications and rotations, then introduces two basic packing techniques and a computational model. ### Source excerpt In my recent overview of homomorphic encryption, I underemphasized the importance of data layout when working with arithmetic (SIMD-style) homomorphic encryption schemes. In the FHE world, the name given to data layout strategies is called "packing," because it revolves around putting multiple plaintext data into RLWE ciphertexts in carefully-chosen ways that mesh well with the operations you'd like to perform. By "mesh well" I mean it reduces the number of extra multiplications and rotations required merely to align data elements properly, rather than doing the actual computation you care about. ## Converting Between Packings in SIMD-Style FHE DevFeed: [Converting Between Packings in SIMD-Style FHE]() Original publisher: [Read original article]() Published: 2024-09-02T21:01:03Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [FHE](), [homomorphic encryption](), [data](), [Code]() Tags: [compilers](), [encryption](), [fhe](), [github](), [graph-coloring](), [heir](), [homomorphic-encryption](), [mathematics](), [optimization](), [packing](), [permutation](), [programming](), [rlwe](), [simd]() ### AI overview This article explains packing in SIMD-style fully homomorphic encryption and focuses on converting between established packings. It introduces a computational model involving RLWE ciphertext vectors, elementwise operations, cyclic rotations, and differing operation costs. ### Source excerpt In my recent overview of homomorphic encryption, I underemphasized the importance of data layout when working with arithmetic (SIMD-style) homomorphic encryption schemes. In the FHE world, the name given to data layout strategies is called "packing," because it revolves around putting multiple plaintext data into RLWE ciphertexts in carefully-chosen ways that mesh well with the operations you'd like to perform. By "mesh well" I mean it reduces the number of extra multiplications and rotations required merely to align data elements properly, rather than doing the actual computation you care about. ## Improving Hugging Face Training Efficiency Through Packing with Flash Attention 2 DevFeed: [Improving Hugging Face Training Efficiency Through Packing with Flash Attention 2]() Original publisher: [Read original article]() Author: Rhui Dih Lee; Arthur Zucker; Achintya Kundu; Laura Wynter; Raghu Ganti; Mayank Mishra Published: 2024-08-21T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [hugging face](), [Transformers](), [Machine learning](), [GPU](), [datasets]() Tags: [datasets](), [efficiency](), [flash-attention-2](), [gpu](), [hugging-face](), [models](), [packing](), [padding](), [training](), [transformers]() ### AI overview The article explains how Hugging Face improves training throughput by packing instruction-tuning sequences without padding while using Flash Attention 2. A new data collator preserves sequence boundaries and can provide up to 2x higher throughput while maintaining convergence quality, with measured gains varying by dataset and sequence-length variance. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science.