return resp.ValueOrZero(), err
}

// GetCPUs - fetch CPU information for a remote node.
func (client *peerRESTClient) GetCPUs(ctx context.Context) (info madmin.CPUs, err error) {
	resp, err := getCPUsHandler.Call(ctx, client.gridConn(), grid.NewMSS())
	return resp.ValueOrZero(), err
}

// GetNetInfo - fetch network information for a remote node.

- 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...

   char *s = s1;
 
   /* Find the end of S1.  */
-  do
-    c = *s1++;
-  while (c != '\0');
-
-  /* Make S1 point before next character, so we can increment
-     it while memory is read (wins on pipelined cpus).  */
-  s1 -= 2;
+  s1 += strlen (s1);
 
-  if (n >= 4)
-    {
-      size_t n4 = n >> 2;
-      do
-	{
-	  c = *s2++;
-	  *++s1 = c;
-	  if (c == '\0')
-	    return s;

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

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.)

- 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))...

- Fixes regression in CPUManager that caused freeing of exclusive CPUs at incorrect times ([#90377](https://github.com/kubernetes/kubernetes/pull/90377), [@cbf123](https://github.com/cbf123)) [SIG Cloud Provider and Node]

- User can choose a different static policy option `SpreadPhysicalCPUsPreferredOption` to spread cpus across physical cpus for some specific applications ([#123733](https://github.com/kubernetes/kubernetes/pull/123733), [@Jeffwan](https://github.com/Jeffwan)) [SIG Node]