settingsFile << """
            rootProject.name = 'app'
        """
        buildFile << """
            apply plugin: 'cpp-application'

            application {
                targetMachines = [machines.host().x86, machines.host().x86_64]
            }
        """
        def headerDir = file('src/main/headers')
        def src1 = file('src/main/cpp/app.cpp').createFile()

Linux containers run using the same Linux kernel of the host (machine, virtual machine, cloud server, etc). This just means that they are very lightweight (compared to full virtual machines emulating an entire operating system).

This way, containers consume **little resources**, an amount comparable to running the processes directly (a virtual machine would consume much more).

            details == 'If you don\'t declare the normalization, outputs can\'t be re-used between machines or locations on the same machine, therefore caching efficiency drops significantly'
            solutions == [ 'Declare the normalization strategy by annotating the property with either @PathSensitive, @Classpath or @CompileClasspath' ]

            details == 'If you don\'t declare the normalization, outputs can\'t be re-used between machines or locations on the same machine, therefore caching efficiency drops significantly'
            solutions == [ 'Declare the normalization strategy by annotating the property with either @PathSensitive, @Classpath or @CompileClasspath' ]

    }

    def "matches J9 toolchain via vendor"() {
        given:
        def queryService = createQueryServiceWithInstallations(
            ["8.hs-amazon", "8.j9-international business machines corporation"],
            null,
            version -> version.split("\\.")[0],
            version -> version.split("-")[1]
        )

        when:

	return pending
}

func testTimerChan(t *testing.T, tim timer, C <-chan Time, synctimerchan bool) {
	_, isTimer := tim.(*Timer)
	isTicker := !isTimer

	// Retry parameters. Enough to deflake even on slow machines.
	// Windows in particular has very coarse timers so we have to
	// wait 10ms just to make a timer go off.
	const (
		sched      = 10 * Millisecond
		tries      = 100
		drainTries = 5
	)

But instead of being limited to the previous build in the same workspace, task output caching allows Gradle to reuse task outputs from any earlier build in any location on the local machine.
When using a shared build cache for task output caching this even works across developer machines and build agents.

If you execute Gradle builds from within your IDE, you already use the Gradle Daemon.
There is no need to enable it for your environment.

== Continuous Integration

We recommend using the Daemon for developer machines and Continuous Integration (CI) servers.

[[deamon_compatibility]]
== Compatibility

Gradle starts a new Daemon if no idle or compatible Daemons exist.

The following values determine compatibility:

			replacement: []byte("$1/test-path/$2"),
		},
		{
			// Example: "customConfigFile":"../../tools/packaging/common/envoy_bootstrap.json"
			// The path may change in CI/other machines
			pattern:     regexp.MustCompile(`("customConfigFile":").*(envoy_bootstrap.json")`),
			replacement: []byte(`"customConfigFile":"envoy_bootstrap.json"`),
		},
		{
			pattern:     regexp.MustCompile(tmpDir),

// the ptr object regardless of the slot's color.
//
// Another place where we intentionally omit memory barriers is when
// accessing mheap_.arena_used to check if a pointer points into the
// heap. On relaxed memory machines, it's possible for a mutator to
// extend the size of the heap by updating arena_used, allocate an
// object from this new region, and publish a pointer to that object,