1,
		"Used to adjust the concurrency of SidecarScope conversions. "+
			"When istiod is deployed on a multi-core CPU server, increasing this value will help to use the CPU to "+
			"accelerate configuration push, but it also means that istiod will consume more CPU resources.",
	).Get()

	MutexProfileFraction = env.Register("MUTEX_PROFILE_FRACTION", 1000,

[[sec:parallel_execution]]
== Parallel projects

Gradle's parallel execution feature optimizes CPU utilization to accelerate builds by concurrently executing tasks from different projects.

		writeErrorResponse(ctx, w, toAPIError(ctx, err), r.URL)
		return
	}

	writeErrorResponse(ctx, w, errorCodes.ToAPIErr(ErrNoSuchWebsiteConfiguration), r.URL)
}

// GetBucketAccelerate  - GET bucket accelerate, a dummy api
func (api objectAPIHandlers) GetBucketAccelerateHandler(w http.ResponseWriter, r *http.Request) {
	ctx := newContext(r, w, "GetBucketAccelerate")

	defer logger.AuditLog(ctx, w, r, mustGetClaimsFromToken(r))

It allows you to easily find out which build produced an artifact coming from the build cache via build scans.

[.screenshot]
image::build-cache/from-cache-origin.png[]

== Accelerate developer builds by reusing CI results

//go:build (!amd64 && !s390x && !ppc64 && !ppc64le && !arm64) || purego

package aes

import (
	"crypto/cipher"
)

// newCipher calls the newCipherGeneric function
// directly. Platforms with hardware accelerated
// implementations of AES should implement their
// own version of newCipher (which may then call
// newCipherGeneric if needed).
func newCipher(key []byte) (cipher.Block, error) {
	return newCipherGeneric(key)
}

#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "mlir/Pass/Pass.h"  // from @llvm-project

namespace mlir {
namespace TFDevice {

// For architectures that support accelerated embedding lookups, this pass will
// rewrite the graph to use pipelining for better device utilization.
std::unique_ptr<OperationPass<ModuleOp>> CreateEmbeddingSequencingPass();

#include "tensorflow/core/framework/device_base.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {

// Helper class for managing data transfers between host and accelerator
// devices using PjRt.
class PjRtDeviceContext : public DeviceContext {
 public:
  explicit PjRtDeviceContext(
      XlaShapeLayoutHelpers::ShapeDeterminationFns shape_determination_fns,

  let summary = "Rewrite graph for embedding pipelining";
  let constructor = "TFDevice::CreateEmbeddingPipeliningPass()";
    let description = [{
    For architectures that support accelerated embedding lookups, this pass will
    rewrite the graph to use pipelining for better device utilization.
  }];
}

def EmbeddingSequencingPass : Pass<"tf-embedding-sequencing", "mlir::ModuleOp"> {

#include "tensorflow/core/common_runtime/local_device.h"
#include "tensorflow/core/framework/device_base.h"

namespace tensorflow {

// tensorflow::PjRtBaseDevice replaces the deprecated tensorflow::XlaDevice.
// This accelerator agnostic device is mainly used to store metadata.
class PjRtBaseDevice : public LocalDevice {
 public:
  // Stores metadata about the PjRtBaseDevice.
  class Metadata {
   public:

    std::function<bool(Operation*)> is_ignored_op) {
  // Iteratively find clusters of different targets within the `block`.
  // Whenever we see an operation that is assigned to an accelerator target
  // (ie. get_target(op) != ""), we try to merge it into the last cluster
  // of same target. If that is infeasible (say because of violating
  // def-before-use), create a new cluster with that operation and move on.