* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.
* **Deep Learning**: this is a sub-field of Machine Learning, so, the same applies. It's just that there is not a single spreadsheet of numbers to multiply, but a huge set of them, and in many cases, you use a special processor to build and / or use those models.

- [`KT-58684`](https://youtrack.jetbrains.com/issue/KT-58684) KJS: ES15 classes — creating instance by class

#### Performance Improvements

- [`KT-58187`](https://youtrack.jetbrains.com/issue/KT-58187) KJS / IR: Huge performance bottleneck while generating sourceMaps (getCannonicalFile)

#### Fixes

- Add huge page stats to Allocated resources in "kubectl describe node" ([#80605](https://github.com/kubernetes/kubernetes/pull/80605), [@odinuge](https://github.com/odinuge)) [SIG CLI]

- The `HugePageStorageMediumSize` feature graduates to GA and unconditionally enabled. Allowing unconditional usage of multiple sizes huge page resources on a container level. ([#99144](https://github.com/kubernetes/kubernetes/pull/99144), [@bart0sh](https://github.com/bart0sh))

    * PodOverhead is an alpha feature as of Kubernetes 1.16.
* Fix kubelet errors in AArch64 with huge page sizes smaller than 1MiB ([#78495](https://github.com/kubernetes/kubernetes/pull/78495), [@odinuge](https://github.com/odinuge))

- Cleanup subnet in frontend IP configs to prevent huge subnet request bodies in some scenarios. ([#98132](https://github.com/kubernetes/kubernetes/pull/98132), [@nilo19](https://github.com/nilo19)) [SIG Cloud Provider]

Go 1.22 changed how the runtime interacts with transparent huge pages on Linux.
In particular, a common default Linux kernel configuration can result in
significant memory overheads, and Go 1.22 no longer works around this default.
To work around this issue without adjusting kernel settings, transparent huge
pages can be disabled for Go memory with the

container based compute environment. A cloud-native application is portable and resilient by design, and can scale horizontally by simply replicating. Modern orchestration platforms like Kubernetes, DC/OS make replicating and managing containers in huge clusters easier than ever.

While containers provide isolated application execution environment, orchestration platforms allow seamless scaling by helping replicate and manage containers. MinIO extends this by adding isolated storage environment...

It used custom types in its declarations instead of standard Python types, but it was still a huge step forward.

It also was one of the first frameworks to generate a custom schema declaring the whole API in JSON.

variety of use-cases, including network latency sensitive applications, as well as applications that benefit from CPU cache residency.  Next, [Huge Pages](https://kubernetes.io/docs/tasks/manage-hugepages/scheduling-hugepages/), which allows pods to consume either 2Mi or 1Gi Huge Pages.  This benefits applications that consume large amounts of memory.  Use of Huge Pages is a common tuning recommendation for databases and JVMs.  Finally, the [Device Plugin](https://kubernetes.io/docs/concepts/cluster-...