import com.vanniktech.maven.publish.JavadocJar
import com.vanniktech.maven.publish.KotlinJvm
import org.graalvm.buildtools.gradle.dsl.GraalVMExtension
import ru.vyarus.gradle.plugin.animalsniffer.AnimalSnifferExtension

plugins {
  kotlin("jvm")
  id("org.jetbrains.dokka")
  id("com.vanniktech.maven.publish.base")
  id("com.gradleup.shadow")
}

val testJavaVersion = System.getProperty("test.java.version", "21").toInt()

import org.jetbrains.kotlin.gradle.dsl.JvmTarget
import org.jetbrains.kotlin.gradle.dsl.kotlinExtension
import org.jetbrains.kotlin.gradle.targets.jvm.tasks.KotlinJvmTest
import org.jetbrains.kotlin.gradle.tasks.KotlinCompile
import ru.vyarus.gradle.plugin.animalsniffer.AnimalSnifferExtension
import java.net.URI

buildscript {
  dependencies {
    classpath(libs.gradlePlugin.dokka)
    classpath(libs.gradlePlugin.kotlin)

		}
	}
	uaAppend("; ", "")

	if cpus, err := gopsutilcpu.Info(); err == nil && len(cpus) > 0 {
		cpuMap := make(map[string]struct{}, len(cpus))
		coreMap := make(map[string]struct{}, len(cpus))
		for i := range cpus {
			cpuMap[cpus[i].PhysicalID] = struct{}{}
			coreMap[cpus[i].CoreID] = struct{}{}
		}
		cpu := cpus[0]

## Recap { #recap }

You can use multiple worker processes with the `--workers` CLI option with the `fastapi` or `uvicorn` commands to take advantage of **multi-core CPUs**, to run **multiple processes in parallel**.

You could use these tools and ideas if you are setting up **your own deployment system** while taking care of the other deployment concepts yourself.

			peersLogOnceIf(logger.SetReqInfo(ctx, reqInfo), nErr.Err, nErr.Host.String())
		}
	}
}

// GetCPUs - Get all CPU information.
func (sys *NotificationSys) GetCPUs(ctx context.Context) []madmin.CPUs {
	reply := make([]madmin.CPUs, len(sys.peerClients))

	g := errgroup.WithNErrs(len(sys.peerClients))
	for index, client := range sys.peerClients {
		if client == nil {
			continue
		}
		index := index

///

## 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**.

| Path                        | Description                                        |
|-----------------------------|----------------------------------------------------|
| `/system/cpu`               | Metrics about CPUs on the system.                  |
| `/system/drive`             | Metrics about drives on the system.                |
| `/system/network/internode` | Metrics about internode requests made by the node. |

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

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

</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

    *   **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.)