///

## Resource Utilization { #resource-utilization }

Your server(s) is (are) a **resource**, you can consume or **utilize**, with your programs, the computation time on the CPUs, and the RAM memory available.

How much of the system resources do you want to be consuming/utilizing? It might be easy to think "not much", but in reality, you will probably want to consume **as much as possible without crashing**.

</p>

<p>
Note that the prohibition on introducing data races
does not apply if the compiler can prove that the races
do not affect correct execution on the target platform.
For example, on essentially all CPUs, it is valid to rewrite
</p>

<pre>
n := 0
for i := 0; i < m; i++ {
	n += *shared
}
</pre>

into:

<pre>
n := 0
local := *shared
for i := 0; i < m; i++ {
	n += local

- A new kubelet command line option, `--reserved-cpus`, is introduced to explicitly define the CPU list that will be reserved for system. For example, if `--reserved-cpus=0,1,2,3` is specified, then cpu 0,1,2,3 will be reserved for the system.  On a system with 24 CPUs, the user may specify `isolcpus=4-23` for the kernel option and use CPU 4-23 for the user containers. ([#83592](https://github.com/kubernet...

- When the kubelet fails to assign CPUs to a Pod because there less available CPUs than the Pod requests, the error message changed from

------------------------------------------------------------------

=== Load balancing and caches.

By default the tests run on multiple processes using all the available cores on all
available CPUs. Not including hyper-threading.
If you want to explicitly specify the number of JVMs you can do so on the command
line:

----------------------------
./gradlew test -Dtests.jvms=8
----------------------------

common:apple-toolchain --crosstool_top=@local_config_apple_cc//:toolchain
common:apple-toolchain --host_crosstool_top=@local_config_apple_cc//:toolchain

# Settings for MacOS on ARM CPUs.
common:macos_arm64 --cpu=darwin_arm64
common:macos_arm64 --macos_minimum_os=11.0
common:macos_arm64 --config=clang_local
common:macos_arm64 --platforms=@build_bazel_apple_support//platforms:darwin_arm64

## Resumen { #recap }

Puedes usar múltiples worker processes con la opción CLI `--workers` con los comandos `fastapi` o `uvicorn` para aprovechar los **CPUs de múltiples núcleos**, para ejecutar **múltiples procesos en paralelo**.

    *   **Linux x86 packages:**
        *   oneDNN optimizations are *enabled by default* on CPUs with
            neural-network-focused hardware features such as AVX512_VNNI,
            AVX512_BF16, AMX, etc.
            ([Intel Cascade Lake](https://www.intel.com/content/www/us/en/products/platforms/details/cascade-lake.html)
            and newer CPUs.)

- Add a new `distribute-cpus-across-numa` option to the static `CPUManager` policy. When enabled, this will trigger the `CPUManager` to evenly distribute CPUs across NUMA nodes in cases where more than one NUMA node is required to satisfy the allocation. ([#105631](https://github.com/kubernetes/kubernetes/pull/105631), [@klueska](https://github.com/klueska))...

## Recapitular { #recap }

Você pode usar vários processos de trabalho com a opção CLI `--workers` com os comandos `fastapi` ou `uvicorn` para aproveitar as vantagens de **CPUs multi-core** e executar **vários processos em paralelo**.

Você pode usar essas ferramentas e ideias se estiver configurando **seu próprio sistema de implantação** enquanto cuida dos outros conceitos de implantação.