Key Concepts in Chrome Memory

What‘s so hard about memory? Isn’t it just malloc and free?

Not really. There are lots of differences and subtleties that change per operating system and even per operating system configuration.

Fortunately, these differences mostly disappear when a program is running with sufficient resources.

Unfortunately, the distinctions end up being very relevant when working near out of memory conditions or analyzing overall performance when there is any amount of memory pressure; this makes crafting and interpreting memory statistics hard.

Fortunately, the point of this doc is to give succinct background that will help you ramp up on the subtleties to work in this space. Yes, this is complicated stuff...but don't despair. You work on a multi-process browser implementing the web platform with high security guarantees. Compared to the rest the system, memory is not THAT complicated.

Can you give specific examples of how it's harder than malloc/free?

Here are some example questions that require a more complex view of memory than malloc/free.

  • When Chrome allocates memory, when does it take up swap space?
  • When memory is free()d, when is it made usable by other applications?
  • Is it always safe to touch the memory returned by malloc()?
  • How many heaps does Chrome have?
  • How are memory resources used by the GPU and drivers accounted for?
  • Is that the same on systems where GPU memory isn't shared with main memory?
  • How are shared libraries accounted for? How big of a penalty is there for each process that shares the memory?
  • What types of memory does Task Manager/Activity Monitor/top report?
  • What about the UMA stats?

In many of the above, the answer actually changes per operating system variant. There is at least one major schism between Windows-based machines and more unixy systems. For example, it is impossible to return all resources (physical ram as well as swap space) to the OS in a way brings them back on demand which drastically changes the way one can handle free lists.

However, even in macOS, Android, CrOS, and “standard desktop linux” each also have enough divergences (compressed memory, pagefile vs swap partition vs no swap, overcommit settings, memory perssure signals etc) that even answering “how much memory is Chromium using” is hard to do in a uniform manner.

The goal of this document is to give a common set of vocabulary and concepts such that Chromium developers can more discuss questions like the ones above without misunderstanding each other.

Key gotchas

Windows allocation uses resources immediately; other OSes use it on first touch.

Arguably the biggest difference for Windows and other OSes is memory granted to a process is always “committed” on allocation. Pragmatically this means that in Windows, malloc(10*1024*1024*1024) will immediately prevent other applications from being able to successfully allocate memory thereby causing them to crash or not be able to open. In Unix variants, usage usually only consumes system resources [TODO(awong): Link to overcommit] when pages are touched.

Not being aware of this difference can cause architecture choices that have a larger than expected resource impact on Windows and incorrect interpretation for metrics on Windows

See the following section on “discardable” memory for more info.

Because of the commit guarantee difference, “discarding” memory has completely different meanings across platforms.

In Unix systems, there is an madvise() function via which pages that have been committed via usage can be returned to the non-resource consuming state. Such a page will then be recommitted on demand making it a tempting optimization for data structures with freelists. However, there is no such API on Windows. The VirtualAlloc(MEM_RESET), DiscardVirtualMemory(), and OfferVirtualMemory() look temptingly similar and on first glance they even look like they work because they will immediately reduce the amount of physical ram (aka Working Set) a processes uses. However, they do NOT release swap meaning they will not help prevent OOM scenarios.

Designing a freelist structure that conflates this behavior (see this PartitionAlloc bug) will result in a system that only truly reduces resource usage on Unix-like systems.

Terms and definitions

TODO(awong): To through Erik's Consistent Memory Metrics doc and pull out bits that reconcile with this.

Commited Memory

Discardable memory

Proportional Set Size

Image memory

Shared Memory.

TODO(awong): Write overview of our platform diversity, windows vs *nix memory models (eg, “committed” memory), what “discardable” memory is, GPU memory, zram, overcommit, the various Chrome heaps (pageheap, partitionalloc, oilpan, v8, malloc...per platform), etc.