Allocation sampling for paged/lo spaces

src/profiler/sampling-heap-profiler.cc

 // Copyright 2015 the V8 project authors. All rights reserved.
 // Use of this source code is governed by a BSD-style license that can be
 // found in the LICENSE file.
 
 #include "src/profiler/sampling-heap-profiler.h"
 
 #include <stdint.h>
 #include <memory>
 #include "src/api.h"
 #include "src/base/utils/random-number-generator.h"
 #include "src/frames-inl.h"
 #include "src/heap/heap.h"
 #include "src/isolate.h"
 #include "src/profiler/strings-storage.h"
 
 namespace v8 {
 namespace internal {
 
+
+// We sample with a Poisson process, with constant average sampling interval.
+// This follows the exponential probability distribution with parameter
+// λ = 1/rate where rate is the average number of bytes between samples.
+//
+// Let u be a uniformly distributed random number between 0 and 1, then
+// next_sample = (- ln u) / λ
+intptr_t SamplingAllocationObserver::GetNextSampleInterval(
+    base::RandomNumberGenerator* random, uint64_t rate) {
+  if (FLAG_sampling_heap_profiler_suppress_randomness) {
+    return rate;
+  }
+  double u = random->NextDouble();
+  double next = (-std::log(u)) * rate;
+  return next < kPointerSize
+             ? kPointerSize
+             : (next > INT_MAX ? INT_MAX : static_cast<intptr_t>(next));
+}
+
+
 SamplingHeapProfiler::SamplingHeapProfiler(Heap* heap, StringsStorage* names,
                                            uint64_t rate, int stack_depth)
-    : InlineAllocationObserver(GetNextSampleInterval(
-          heap->isolate()->random_number_generator(), rate)),
-      isolate_(heap->isolate()),
+    : isolate_(heap->isolate()),
       heap_(heap),
-      random_(isolate_->random_number_generator()),
       names_(names),
       samples_(),
-      rate_(rate),
       stack_depth_(stack_depth) {
-  heap->new_space()->AddInlineAllocationObserver(this);
+  new_space_observer_ = new SamplingAllocationObserver(
+      heap_, rate, rate, this, heap->isolate()->random_number_generator());
+  heap->new_space()->AddInlineAllocationObserver(new_space_observer_);
+  other_spaces_observer_ = new SamplingAllocationObserver(
+      heap_, rate, rate, this, heap->isolate()->random_number_generator());
+  heap->old_space()->AddAllocationObserver(other_spaces_observer_);
+  heap->code_space()->AddAllocationObserver(other_spaces_observer_);
+  heap->map_space()->AddAllocationObserver(other_spaces_observer_);
+  heap->lo_space()->AddAllocationObserver(other_spaces_observer_);
 }
 
 
 SamplingHeapProfiler::~SamplingHeapProfiler() {
-  heap_->new_space()->RemoveInlineAllocationObserver(this);
+  heap_->new_space()->RemoveInlineAllocationObserver(new_space_observer_);
+  heap_->old_space()->RemoveAllocationObserver(other_spaces_observer_);
+  heap_->code_space()->RemoveAllocationObserver(other_spaces_observer_);
+  heap_->map_space()->RemoveAllocationObserver(other_spaces_observer_);
+  heap_->lo_space()->RemoveAllocationObserver(other_spaces_observer_);
 
   // Clear samples and drop all the weak references we are keeping.
   std::set<SampledAllocation*>::iterator it;
   for (it = samples_.begin(); it != samples_.end(); ++it) {
     delete *it;
   }
   std::set<SampledAllocation*> empty;
   samples_.swap(empty);
 }
 
-void SamplingHeapProfiler::Step(int bytes_allocated, Address soon_object,
-                                size_t size) {
-  DCHECK(heap_->gc_state() == Heap::NOT_IN_GC);
-  DCHECK(soon_object);
-  SampleObject(soon_object, size);
-}
-
 
 void SamplingHeapProfiler::SampleObject(Address soon_object, size_t size) {
   DisallowHeapAllocation no_allocation;
 
   HandleScope scope(isolate_);
   HeapObject* heap_object = HeapObject::FromAddress(soon_object);
   Handle<Object> obj(heap_object, isolate_);
 
   // Mark the new block as FreeSpace to make sure the heap is iterable while we
   // are taking the sample.
   heap()->CreateFillerObjectAt(soon_object, static_cast<int>(size));
 
   Local<v8::Value> loc = v8::Utils::ToLocal(obj);
 
   SampledAllocation* sample =
       new SampledAllocation(this, isolate_, loc, size, stack_depth_);
   samples_.insert(sample);
 }
 
 
-// We sample with a Poisson process, with constant average sampling interval.
-// This follows the exponential probability distribution with parameter
-// λ = 1/rate where rate is the average number of bytes between samples.
-//
-// Let u be a uniformly distributed random number between 0 and 1, then
-// next_sample = (- ln u) / λ
-intptr_t SamplingHeapProfiler::GetNextSampleInterval(
-    base::RandomNumberGenerator* random, uint64_t rate) {
-  if (FLAG_sampling_heap_profiler_suppress_randomness) {
-    return rate;
-  }
-  double u = random->NextDouble();
-  double next = (-std::log(u)) * rate;
-  return next < kPointerSize
-             ? kPointerSize
-             : (next > INT_MAX ? INT_MAX : static_cast<intptr_t>(next));
-}
-
-
 void SamplingHeapProfiler::SampledAllocation::OnWeakCallback(
     const WeakCallbackInfo<SampledAllocation>& data) {
   SampledAllocation* sample = data.GetParameter();
   sample->sampling_heap_profiler_->samples_.erase(sample);
   delete sample;
 }
 
 
 SamplingHeapProfiler::FunctionInfo::FunctionInfo(SharedFunctionInfo* shared,
                                                  StringsStorage* names)
@@ skipping 142 matching lines @@
     Node* node = AddStack(profile, scripts, allocation->get_stack());
     node->allocations.push_back({allocation->get_size(), 1});
   }
 
   return profile;
 }
 
 
 }  // namespace internal
 }  // namespace v8