Merge pull request #11360 from maurelian/patch-2

Some improvements to optimizer documentation

docs/internals/optimizer.rst

@@ -6,24 +6,24 @@ The Optimizer
 *************
 
 The Solidity compiler uses two different optimizer modules: The "old" optimizer
-that operates at opcode level and the "new" optimizer that operates on Yul IR code.
+that operates at the opcode level and the "new" optimizer that operates on Yul IR code.
 
 The opcode-based optimizer applies a set of `simplification rules <https://github.com/ethereum/solidity/blob/develop/libevmasm/RuleList.h>`_
 to opcodes. It also combines equal code sets and removes unused code.
 
 The Yul-based optimizer is much more powerful, because it can work across function
-calls: In Yul, it is not possible to perform arbitrary jumps, so it is for example
+calls. For example, arbitrary jumps are not possible in Yul, so it is
 possible to compute the side-effects of each function. Consider two function calls,
-where the first does not modify the storage and the second modifies the storage.
+where the first does not modify storage and the second does modify storage.
-If their arguments and return values does not depend on each other, we can reorder
+If their arguments and return values do not depend on each other, we can reorder
 the function calls. Similarly, if a function is
 side-effect free and its result is multiplied by zero, you can remove the function
 call completely.
 
 Currently, the parameter ``--optimize`` activates the opcode-based optimizer for the
 generated bytecode and the Yul optimizer for the Yul code generated internally, for example for ABI coder v2.
-One can use ``solc --ir-optimized --optimize`` to produce
+One can use ``solc --ir-optimized --optimize`` to produce an
-optimized experimental Yul IR for a Solidity source. Similarly, use ``solc --strict-assembly --optimize``
+optimized experimental Yul IR for a Solidity source. Similarly, one can use ``solc --strict-assembly --optimize``
 for a stand-alone Yul mode.
 
 You can find more details on both optimizer modules and their optimization steps below.
@@ -32,7 +32,7 @@ Benefits of Optimizing Solidity Code
 ====================================
 
 Overall, the optimizer tries to simplify complicated expressions, which reduces both code
-size and execution cost, i.e., it can reduce gas needed for contract deployment as well as for external calls to the contract.
+size and execution cost, i.e., it can reduce gas needed for contract deployment as well as for external calls made to the contract.
 It also specializes or inlines functions. Especially
 function inlining is an operation that can cause much bigger code, but it is
 often done because it results in opportunities for more simplifications.
@@ -41,11 +41,11 @@ often done because it results in opportunities for more simplifications.
 Differences between Optimized and Non-Optimized Code
 ====================================================
 
-Generally, the most visible difference would be constant expressions getting evaluated.
+Generally, the most visible difference is that constant expressions are evaluated at compile time.
-When it comes to the ASM output, one can also notice reduction of equivalent/duplicate
+When it comes to the ASM output, one can also notice a reduction of equivalent or duplicate
-"code blocks" (compare the output of the flags ``--asm`` and ``--asm --optimize``). However,
+code blocks (compare the output of the flags ``--asm`` and ``--asm --optimize``). However,
 when it comes to the Yul/intermediate-representation, there can be significant
-differences, for example, functions can get inlined, combined, rewritten to eliminate
+differences, for example, functions may be inlined, combined, or rewritten to eliminate
 redundancies, etc. (compare the output between the flags ``--ir`` and
 ``--optimize --ir-optimized``).
 
@@ -55,7 +55,9 @@ Optimizer Parameter Runs
 The number of runs (``--optimize-runs``) specifies roughly how often each opcode of the
 deployed code will be executed across the life-time of the contract. This means it is a
 trade-off parameter between code size (deploy cost) and code execution cost (cost after deployment).
-A "runs" parameter of "1" will produce short but expensive code. The largest value is ``2**32-1``.
+A "runs" parameter of "1" will produce short but expensive code. In contrast, a larger "runs"
+parameter will produce longer but more gas efficient code. The maximum value of the parameter
+is ``2**32-1``.
 
 .. note::
 
@@ -65,31 +67,81 @@ A "runs" parameter of "1" will produce short but expensive code. The largest val
 Opcode-Based Optimizer Module
 =============================
 
-The opcode-based optimizer module operates on assembly. It splits the
+The opcode-based optimizer module operates on assembly code. It splits the
 sequence of instructions into basic blocks at ``JUMPs`` and ``JUMPDESTs``.
 Inside these blocks, the optimizer analyzes the instructions and records every modification to the stack,
 memory, or storage as an expression which consists of an instruction and
-a list of arguments which are pointers to other expressions. The opcode-based optimizer
+a list of arguments which are pointers to other expressions.
-uses a component called "CommonSubexpressionEliminator" that amongst other
+
+Additionally, the opcode-based optimizer
+uses a component called "CommonSubexpressionEliminator" that, amongst other
 tasks, finds expressions that are always equal (on every input) and combines
 them into an expression class. It first tries to find each new
 expression in a list of already known expressions. If no such matches are found,
 it simplifies the expression according to rules like
 ``constant + constant = sum_of_constants`` or ``X * 1 = X``. Since this is
 a recursive process, we can also apply the latter rule if the second factor
-is a more complex expression where we know that it always evaluates to one.
+is a more complex expression which we know always evaluates to one.
-Modifications to storage and memory locations have to erase knowledge about
+
-storage and memory locations which are not known to be different. If we first
+Certain optimizer steps symbolically track the storage and memory locations. For example, this
-write to location x and then to location y and both are input variables, the
+information is used to compute Keccak-256 hashes that can be evaluated during compile time. Consider
-second could overwrite the first, so we do not know what is stored at x after
+the sequence:
-we wrote to y. If simplification of the expression ``x - y`` evaluates to a
+
-non-zero constant, we know that we can keep our knowledge about what is stored at ``x``.
+::
+
+  PUSH 32
+  PUSH 0
+  CALLDATALOAD
+  PUSH 100
+  DUP2
+  MSTORE
+  KECCAK256
+
+or the equivalent Yul
+
+::
+
+  let x := calldataload(0)
+  mstore(x, 100)
+  let value := keccak256(x, 32)
+
+In this case, the optimizer tracks the value at a memory location ``calldataload(0)`` and then
+realizes that the Keccak-256 hash can be evaluated at compile time. This only works if there is no
+other instruction that modifies memory between the ``mstore`` and ``keccak256``. So if there is an
+instruction that writes to memory (or storage), then we need to erase the knowledge of the current
+memory (or storage). There is, however, an exception to this erasing, when we can easily see that
+the instruction doesn't write to a certain location.
+
+For example,
+
+::
+
+  let x := calldataload(0)
+  mstore(x, 100)
+  // Current knowledge memory location x -> 100
+  let y := add(x, 32)
+  // Does not clear the knowledge that x -> 100, since y does not write to [x, x + 32)
+  mstore(y, 200)
+  // This Keccak-256 can now be evaluated
+  let value := keccak256(x, 32)
+
+Therefore, modifications to storage and memory locations, of say location ``l``, must erase
+knowledge about storage or memory locations which may be equal to ``l``. More specifically, for
+storage, the optimizer has to erase all knowledge of symbolic locations, that may be equal to ``l``
+and for memory, the optimizer has to erase all knowledge of symbolic locations that may not be at
+least 32 bytes away. If ``m`` denotes an arbitrary location, then this decision on erasure is done
+by computing the value ``sub(l, m)``. For storage, if this value evaluates to a literal that is
+non-zero, then the knowledge about ``m`` will be kept. For memory, if the value evaluates to a
+literal that is between ``32`` and ``2**256 - 32``, then the knowledge about ``m`` will be kept. In
+all other cases, the knowledge about ``m`` will be erased.
 
 After this process, we know which expressions have to be on the stack at
 the end, and have a list of modifications to memory and storage. This information
 is stored together with the basic blocks and is used to link them. Furthermore,
 knowledge about the stack, storage and memory configuration is forwarded to
-the next block(s). If we know the targets of all ``JUMP`` and ``JUMPI`` instructions,
+the next block(s).
+
+If we know the targets of all ``JUMP`` and ``JUMPI`` instructions,
 we can build a complete control flow graph of the program. If there is only
 one target we do not know (this can happen as in principle, jump targets can
 be computed from inputs), we have to erase all knowledge about the input state
@@ -108,19 +160,18 @@ stack in the correct place.
 These steps are applied to each basic block and the newly generated code
 is used as replacement if it is smaller. If a basic block is split at a
 ``JUMPI`` and during the analysis, the condition evaluates to a constant,
-the ``JUMPI`` is replaced depending on the value of the constant. Thus code like
+the ``JUMPI`` is replaced based on the value of the constant. Thus code like
 
 ::
 
     uint x = 7;
     data[7] = 9;
-    if (data[x] != x + 2)
+    if (data[x] != x + 2) // this condition is never true
       return 2;
     else
       return 1;
 
-still simplifies to code which you can compile even though the instructions contained
+simplifies to this:
-a jump in the beginning of the process:
 
 ::