Merge pull request #17 from JuliaComputing/dg/readme

Add README

Author: DhairyaLGandhi

README.md

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+# SymbolicCompilerPasses.jl 
+
+[![Join the chat at https://julialang.zulipchat.com #sciml-bridged](https://img.shields.io/static/v1?label=Zulip&message=chat&color=9558b2&labelColor=389826)](https://julialang.zulipchat.com/#narrow/stream/279055-sciml-bridged)
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+[![Build Status](https://github.com/JuliaComputing/SymbolicCompilerPasses.jl/actions/workflows/ci.yml/badge.svg)](https://github.com/JuliaComputing/SymbolicCompilerPasses.jl/actions/workflows/ci.yml)
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+[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor%27s%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
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+
+Compiler Pass Plugins for Symbolic Expressions. Designed to work with array heavy code.
+
+## ModelingToolkit Integration
+
+```julia
+using Multibody
+import ModelingToolkit as MTK
+
+t = Multibody.t
+world = Multibody.world
+
+@named joint = Revolute(n = Float64[0, 0, 1], isroot = true);
+@named body = Body(; m = 1, isroot = false, r_cm = [0.5, 0, 0])
+
+connections = [
+    connect(world.frame_b, joint.frame_a)
+    connect(joint.frame_b, body.frame_a)
+]
+
+@named model = System(connections, t, systems=[world, joint, body])
+ssys = multibody(model)
+
+D = Differential(t)
+
+prob = ODEProblem(ssys, defs, (0, 3.35),
+        optimize = MTK.SCP_BASIC
+)
+```
+
+
+A list of passes can be passed as well.
+
+```julia
+import SymbolicCompilerPasses as SC
+
+prob = ODEProblem(ssys, defs, (0, 3.35),
+        optimize = [SC.LDIV_RULE,
+                    SC.HVNCAT_STATIC_RULE,
+                    SC.MB_VIEW_RULE,
+                    SC.MATMUL_ADD_RULE]
+)
+```
+
+Lower level API gives more granular control over applying passes to standard symbolic expressions.
+
+```julia
+julia> using SymbolicUtils
+
+julia> using SymbolicUtils.Code
+
+julia> import SymbolicUtils as SU
+
+julia> import SymbolicCompilerPasses as SC
+
+julia> @syms A[1:3, 1:3] B[1:3, 1:3] C[1:3, 1:3] D[1:3, 1:3] E[1:3, 1:3]
+(A, B, C, D, E)
+
+julia> expr = A * B + C
+C + A*B
+
+julia> current = Code.cse(expr)
+Let(Union{Assignment, DestructuredArgs}[Assignment(var"##cse#1", A*B), Assignment(var"##cse#2", var"##cse#1" + C)], var"##cse#2", false)
+
+julia> toexpr(current)
+quote
+    var"##cse#1" = (*)(A, B)
+    var"##cse#2" = (+)(var"##cse#1", C)
+    var"##cse#2"
+end
+
+julia> state = Code.CSEState();
+
+julia> optimized = Code.apply_optimization_rule(current, state, SC.MATMUL_ADD_RULE)
+Let(Union{Assignment, DestructuredArgs}[Assignment(var"##mul5_temp#1", SymbolicCompilerPasses.get_from_cache(C)), Assignment(var"##mul5_temp#1", LinearAlgebra.mul!(var"##mul5_temp#1", A, B, 1, 1)), Assignment(var"##mul5_temp#1", var"##mul5_temp#1")], var"##mul5_temp#1", false)
+
+julia> toexpr(optimized)
+quote
+    var"##mul5_temp#1" = (SymbolicCompilerPasses.get_from_cache)(C)
+    var"##mul5_temp#1" = (LinearAlgebra.mul!)(var"##mul5_temp#1", A, B, 1, 1)
+    var"##mul5_temp#1" = var"##mul5_temp#1"
+    var"##mul5_temp#1"
+end
+```