# MemorySSA

## Introduction

`MemorySSA` is an analysis that allows us to cheaply reason about the interactions between various memory operations. Its goal is to replace `MemoryDependenceAnalysis` for most (if not all) use-cases. This is because, unless you’re very careful, use of `MemoryDependenceAnalysis` can easily result in quadratic-time algorithms in LLVM. Additionally, `MemorySSA` doesn’t have as many arbitrary limits as `MemoryDependenceAnalysis`, so you should get better results, too. One common use of `MemorySSA` is to quickly find out that something definitely cannot happen (for example, reason that a hoist out of a loop can’t happen).

At a high level, one of the goals of `MemorySSA` is to provide an SSA based form for memory, complete with def-use and use-def chains, which enables users to quickly find may-def and may-uses of memory operations. It can also be thought of as a way to cheaply give versions to the complete state of memory, and associate memory operations with those versions.

This document goes over how `MemorySSA` is structured, and some basic intuition on how `MemorySSA` works.

A paper on MemorySSA (with notes about how it’s implemented in GCC) can be found here. Though, it’s relatively out-of-date; the paper references multiple memory partitions, but GCC eventually swapped to just using one, like we now have in LLVM. Like GCC’s, LLVM’s MemorySSA is intraprocedural.

## MemorySSA Structure

MemorySSA is a virtual IR. After it’s built, `MemorySSA` will contain a structure that maps `Instruction`s to `MemoryAccess`es, which are `MemorySSA`’s parallel to LLVM `Instruction`s.

Each `MemoryAccess` can be one of three types:

- `MemoryDef`

- `MemoryPhi`

- `MemoryUse`

`MemoryDef`s are operations which may either modify memory, or which introduce some kind of ordering constraints. Examples of `MemoryDef`s include `store`s, function calls, `load`s with `acquire` (or higher) ordering, volatile operations, memory fences, etc. A `MemoryDef` always introduces a new version of the entire memory and is linked with a single `MemoryDef/MemoryPhi` which is the version of memory that the new version is based on. This implies that there is a single `Def` chain that connects all the `Def`s, either directly or indirectly. For example in:

    b = MemoryDef(a)
    c = MemoryDef(b)
    d = MemoryDef(c)

`d` is connected directly with `c` and indirectly with `b`. This means that `d` potentially clobbers (see below) `c` or `b` or both. This in turn implies that without the use of The walker, initially every `MemoryDef` clobbers every other `MemoryDef`.

`MemoryPhi`s are `PhiNode`s, but for memory operations. If at any point we have two (or more) `MemoryDef`s that could flow into a `BasicBlock`, the block’s top `MemoryAccess` will be a `MemoryPhi`. As in LLVM IR, `MemoryPhi`s don’t correspond to any concrete operation. As such, `BasicBlock`s are mapped to `MemoryPhi`s inside `MemorySSA`, whereas `Instruction`s are mapped to `MemoryUse`s and `MemoryDef`s.

Note also that in SSA, Phi nodes merge must-reach definitions (that is, definitions that must be new versions of variables). In MemorySSA, PHI nodes merge may-reach definitions (that is, until disambiguated, the versions that reach a phi node may or may not clobber a given variable).

`MemoryUse`s are operations which use but don’t modify memory. An example of a `MemoryUse` is a `load`, or a `readonly` function call.

Every function that exists has a special `MemoryDef` called `liveOnEntry`. It dominates every `MemoryAccess` in the function that `MemorySSA` is being run on, and implies that we’ve hit the top of the function. It’s the only `MemoryDef` that maps to no `Instruction` in LLVM IR. Use of `liveOnEntry` implies that the memory being used is either undefined or defined before the function begins.

An example of all of this overlaid on LLVM IR (obtained by running `opt -passes='print<memoryssa>' -disable-output` on an `.ll` file) is below. When viewing this example, it may be helpful to view it in terms of clobbers. The operands of a given `MemoryAccess` are all (potential) clobbers of said `MemoryAccess`, and the value produced by a `MemoryAccess` can act as a clobber for other `MemoryAccess`es.

If a `MemoryAccess` is a clobber of another, it means that these two `MemoryAccess`es may access the same memory. For example, `x = MemoryDef(y)` means that `x` potentially modifies memory that `y` modifies/constrains (or has modified / constrained). In the same manner, `a = MemoryPhi({BB1,b},{BB2,c})` means that anyone that uses `a` is accessing memory potentially modified / constrained by either `b` or `c` (or both). And finally, `MemoryUse(x)` means that this use accesses memory that `x` has modified / constrained (as an example, think that if `x = MemoryDef(...)` and `MemoryUse(x)` are in the same loop, the use can’t be hoisted outside alone).

Another useful way of looking at it is in terms of memory versions. In that view, operands of a given `MemoryAccess` are the version of the entire memory before the operation, and if the access produces a value (i.e. `MemoryDef/MemoryPhi`), the value is the new version of the memory after the operation.

    define void @foo() {
    entry:
      %p1 = alloca i8
      %p2 = alloca i8
      %p3 = alloca i8
      ; 1 = MemoryDef(liveOnEntry)
      store i8 0, ptr %p3
      br label %while.cond

    while.cond:
      ; 6 = MemoryPhi({entry,1},{if.end,4})
      br i1 undef, label %if.then, label %if.else

    if.then:
      ; 2 = MemoryDef(6)
      store i8 0, ptr %p1
      br label %if.end

    if.else:
      ; 3 = MemoryDef(6)
      store i8 1, ptr %p2
      br label %if.end

    if.end:
      ; 5 = MemoryPhi({if.then,2},{if.else,3})
      ; MemoryUse(5)
      %1 = load i8, ptr %p1
      ; 4 = MemoryDef(5)
      store i8 2, ptr %p2
      ; MemoryUse(1)
      %2 = load i8, ptr %p3
      br label %while.cond
    }

The `MemorySSA` IR is shown in comments that precede the instructions they map to (if such an instruction exists). For example, `1 = MemoryDef(liveOnEntry)` is a `MemoryAccess` (specifically, a `MemoryDef`), and it describes the LLVM instruction `store i8 0, ptr %p3`. Other places in `MemorySSA` refer to this particular `MemoryDef` as `1` (much like how one can refer to `load i8, ptr %p1` in LLVM with `%1`). Again, `MemoryPhi`s don’t correspond to any LLVM Instruction, so the line directly below a `MemoryPhi` isn’t special.

Going from the top down:

- `6 = MemoryPhi({entry,1},{if.end,4})` notes that, when entering `while.cond`, the reaching definition for it is either `1` or `4`. This `MemoryPhi` is referred to in the textual IR by the number `6`.

- `2 = MemoryDef(6)` notes that `store i8 0, ptr %p1` is a definition, and its reaching definition before it is `6`, or the `MemoryPhi` after `while.cond`. (See the Use and Def optimization and Precision sections below for why this `MemoryDef` isn’t linked to a separate, disambiguated `MemoryPhi`.)

- `3 = MemoryDef(6)` notes that `store i8 0, ptr %p2` is a definition; its reaching definition is also `6`.

- `5 = MemoryPhi({if.then,2},{if.else,3})` notes that the clobber before this block could either be `2` or `3`.

- `MemoryUse(5)` notes that `load i8, ptr %p1` is a use of memory, and that it’s clobbered by `5`.

- `4 = MemoryDef(5)` notes that `store i8 2, ptr %p2` is a definition; its reaching definition is `5`.

- `MemoryUse(1)` notes that `load i8, ptr %p3` is just a user of memory, and the last thing that could clobber this use is above `while.cond` (e.g. the store to `%p3`). In memory versioning parlance, it really only depends on the memory version 1, and is unaffected by the new memory versions generated since then.

As an aside, `MemoryAccess` is a `Value` mostly for convenience; it’s not meant to interact with LLVM IR.

## Design of MemorySSA

`MemorySSA` is an analysis that can be built for any arbitrary function. When it’s built, it does a pass over the function’s IR in order to build up its mapping of `MemoryAccess`es. You can then query `MemorySSA` for things like the dominance relation between `MemoryAccess`es, and get the `MemoryAccess` for any given `Instruction` .

When `MemorySSA` is done building, it also hands you a `MemorySSAWalker` that you can use (see below).

### The walker

A structure that helps `MemorySSA` do its job is the `MemorySSAWalker`, or the walker, for short. The goal of the walker is to provide answers to clobber queries beyond what’s represented directly by `MemoryAccess`es. For example, given:

    define void @foo() {
      %a = alloca i8
      %b = alloca i8

      ; 1 = MemoryDef(liveOnEntry)
      store i8 0, ptr %a
      ; 2 = MemoryDef(1)
      store i8 0, ptr %b
    }

The store to `%a` is clearly not a clobber for the store to `%b`. It would be the walker’s goal to figure this out, and return `liveOnEntry` when queried for the clobber of `MemoryAccess` `2`.

By default, `MemorySSA` provides a walker that can optimize `MemoryDef`s and `MemoryUse`s by consulting whatever alias analysis stack you happen to be using. Walkers were built to be flexible, though, so it’s entirely reasonable (and expected) to create more specialized walkers (e.g. one that specifically queries `GlobalsAA`, one that always stops at `MemoryPhi` nodes, etc).

#### Default walker APIs

There are two main APIs used to retrieve the clobbering access using the walker:

- `MemoryAccess *getClobberingMemoryAccess(MemoryAccess *MA);` return the clobbering memory access for `MA`, caching all intermediate results computed along the way as part of each access queried.

- `MemoryAccess *getClobberingMemoryAccess(MemoryAccess *MA, const MemoryLocation &Loc);` returns the access clobbering memory location `Loc`, starting at `MA`. Because this API does not request the clobbering access of a specific memory access, there are no results that can be cached.

#### Locating clobbers yourself

If you choose to make your own walker, you can find the clobber for a `MemoryAccess` by walking every `MemoryDef` that dominates said `MemoryAccess`. The structure of `MemoryDef`s makes this relatively simple; they ultimately form a linked list of every clobber that dominates the `MemoryAccess` that you’re trying to optimize. In other words, the `definingAccess` of a `MemoryDef` is always the nearest dominating `MemoryDef` or `MemoryPhi` of said `MemoryDef`.

### Use and Def optimization

`MemoryUse`s keep a single operand, which is their defining or optimized access. Traditionally `MemorySSA` optimized `MemoryUse`s at build-time, up to a given threshold. Specifically, the operand of every `MemoryUse` was optimized to point to the actual clobber of said `MemoryUse`. This can be seen in the above example; the second `MemoryUse` in `if.end` has an operand of `1`, which is a `MemoryDef` from the entry block. This is done to make walking, value numbering, etc, faster and easier. As of this revision, the default was changed to not optimize uses at build time, in order to provide the option to reduce compile-time if the walking is not necessary in a pass. Most users call the new API `ensureOptimizedUses()` to keep the previous behavior and do a one-time optimization of `MemoryUse`s, if this was not done before. New pass users are recommended to call `ensureOptimizedUses()`.

Initially it was not possible to optimize `MemoryDef`s in the same way, as we restricted `MemorySSA` to one operand per access. This was changed and `MemoryDef`s now keep two operands. The first one, the defining access, is always the previous `MemoryDef` or `MemoryPhi` in the same basic block, or the last one in a dominating predecessor if the current block doesn’t have any other accesses writing to memory. This is needed for walking Def chains. The second operand is the optimized access, if there was a previous call on the walker’s `getClobberingMemoryAccess(MA)`. This API will cache information as part of `MA`. Optimizing all `MemoryDef`s has quadratic time complexity and is not done by default.

A walk of the uses for any MemoryDef can find the accesses that were optimized to it. A code snippet for such a walk looks like this:

    MemoryDef *Def;  // find who's optimized or defining for this MemoryDef
    for (auto &U : Def->uses()) {
      MemoryAccess *MA = cast<MemoryAccess>(U.getUser());
      if (auto *DefUser = dyn_cast<MemoryDef>(MA))
        if (DefUser->isOptimized() && DefUser->getOptimized() == Def) {
          // User who is optimized to Def
        } else {
          // User who's defining access is Def; optimized to something else or not optimized.
        }
    }

When `MemoryUse`s are optimized, for a given store, you can find all loads clobbered by that store by walking the immediate and transitive uses of the store.

    checkUses(MemoryAccess *Def) { // Def can be a MemoryDef or a MemoryPhi.
      for (auto &U : Def->uses()) {
        MemoryAccess *MA = cast<MemoryAccess>(U.getUser());
        if (auto *MU = dyn_cast<MemoryUse>(MA)) {
          // Process MemoryUse as needed.
        } else {
          // Process MemoryDef or MemoryPhi as needed.

          // As a user can come up twice, as an optimized access and defining
          // access, keep a visited list.

          // Check transitive uses as needed
          checkUses(MA); // use a worklist for an iterative algorithm
        }
      }
    }

An example of similar traversals can be found in the DeadStoreElimination pass.

### Invalidation and updating

Because `MemorySSA` keeps track of LLVM IR, it needs to be updated whenever the IR is updated. “Update”, in this case, includes the addition, deletion, and motion of `Instructions`. The update API is being made on an as-needed basis. If you’d like examples, `GVNHoist` and `LICM` are users of `MemorySSA`s update API. Note that adding new `MemoryDef`s (by calling `insertDef`) can be a time-consuming update, if the new access triggers many `MemoryPhi` insertions and renaming (optimization invalidation) of many `MemoryAccesses`es.

#### Phi placement

`MemorySSA` only places `MemoryPhi`s where they’re actually needed. That is, it is a pruned SSA form, like LLVM’s SSA form. For example, consider:

    define void @foo() {
    entry:
      %p1 = alloca i8
      %p2 = alloca i8
      %p3 = alloca i8
      ; 1 = MemoryDef(liveOnEntry)
      store i8 0, ptr %p3
      br label %while.cond

    while.cond:
      ; 3 = MemoryPhi({%0,1},{if.end,2})
      br i1 undef, label %if.then, label %if.else

    if.then:
      br label %if.end

    if.else:
      br label %if.end

    if.end:
      ; MemoryUse(1)
      %1 = load i8, ptr %p1
      ; 2 = MemoryDef(3)
      store i8 2, ptr %p2
      ; MemoryUse(1)
      %2 = load i8, ptr %p3
      br label %while.cond
    }

Because we removed the stores from `if.then` and `if.else`, a `MemoryPhi` for `if.end` would be pointless, so we don’t place one. So, if you need to place a `MemoryDef` in `if.then` or `if.else`, you’ll need to also create a `MemoryPhi` for `if.end`.

If it turns out that this is a large burden, we can just place `MemoryPhi`s everywhere. Because we have Walkers that are capable of optimizing above said phis, doing so shouldn’t prohibit optimizations.

### Non-Goals

`MemorySSA` is meant to reason about the relation between memory operations, and enable quicker querying. It isn’t meant to be the single source of truth for all potential memory-related optimizations. Specifically, care must be taken when trying to use `MemorySSA` to reason about atomic or volatile operations, as in:

    define i8 @foo(ptr %a) {
    entry:
      br i1 undef, label %if.then, label %if.end

    if.then:
      ; 1 = MemoryDef(liveOnEntry)
      %0 = load volatile i8, ptr %a
      br label %if.end

    if.end:
      %av = phi i8 [0, %entry], [%0, %if.then]
      ret i8 %av
    }

Going solely by `MemorySSA`’s analysis, hoisting the `load` to `entry` may seem legal. Because it’s a volatile load, though, it’s not.

### Design tradeoffs

#### Precision

`MemorySSA` in LLVM deliberately trades off precision for speed. Let us think about memory variables as if they were disjoint partitions of the memory (that is, if you have one variable, as above, it represents the entire memory, and if you have multiple variables, each one represents some disjoint portion of the memory)

First, because alias analysis results conflict with each other, and each result may be what an analysis wants (IE TBAA may say no-alias, and something else may say must-alias), it is not possible to partition the memory the way every optimization wants. Second, some alias analysis results are not transitive (IE A noalias B, and B noalias C, does not mean A noalias C), so it is not possible to come up with a precise partitioning in all cases without variables to represent every pair of possible aliases. Thus, partitioning precisely may require introducing at least N^2 new virtual variables, phi nodes, etc.

Each of these variables may be clobbered at multiple def sites.

To give an example, if you were to split up struct fields into individual variables, all aliasing operations that may-def multiple struct fields, will may-def more than one of them. This is pretty common (calls, copies, field stores, etc).

Experience with SSA forms for memory in other compilers has shown that it is simply not possible to do this precisely, and in fact, doing it precisely is not worth it, because now all the optimizations have to walk tons and tons of virtual variables and phi nodes.

So we partition. At the point at which you partition, again, experience has shown us there is no point in partitioning to more than one variable. It simply generates more IR, and optimizations still have to query something to disambiguate further anyway.

As a result, LLVM partitions to one variable.

#### Precision in practice

In practice, there are implementation details in LLVM that also affect the results’ precision provided by `MemorySSA`. For example, AliasAnalysis has various caps, or restrictions on looking through phis which can affect what `MemorySSA` can infer. Changes made by different passes may make MemorySSA either “overly optimized” (it can provide a more accurate result than if it were recomputed from scratch), or “under optimized” (it could infer more if it were recomputed). This can lead to challenges to reproduced results in isolation with a single pass when the result relies on the state acquired by `MemorySSA` due to being updated by multiple subsequent passes. Passes that use and update `MemorySSA` should do so through the APIs provided by the `MemorySSAUpdater`, or through calls on the Walker. Direct optimizations to `MemorySSA` are not permitted. There is currently a single, narrowly scoped exception where DSE (DeadStoreElimination) updates an optimized access of a store, after a traversal that guarantees the optimization is correct. This is solely allowed due to the traversals and inferences being beyond what `MemorySSA` does and them being “free” (i.e. DSE does them anyway). This exception is set under a flag (“-dse-optimize-memoryssa”) and can be disabled to help reproduce optimizations in isolation.

### LLVM Developers Meeting presentations

- 2016 LLVM Developers’ Meeting: G. Burgess - MemorySSA in Five Minutes.

- 2020 LLVM Developers’ Meeting: S. Baziotis & S. Moll - Finding Your Way Around the LLVM Dependence Analysis Zoo