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+# Finch.jl
+
+Finch is a Julia-to-Julia compiler for sparse or structured multidimensional arrays. Finch empowers users to write high-level array programs which are transformed behind-the-scenes into fast sparse code.
+
+## Why Finch.jl?
+
+Finch was built to make sparse and structured array programming easier and more efficient.  Finch.jl leverages compiler technology to automatically generate customized, fused sparse kernels for each specific
+use case. This allows users to write readable, high-level sparse array programs without worrying about the performance of the generated code. Finch can automatically generate efficient implementations even for unique problems that lack existing library solutions.
+
+# Installation
+
+At the [Julia](https://julialang.org/downloads/) REPL, install the latest stable version by running:
+
+```julia
+julia> using Pkg; Pkg.add("Finch")
+```
+
+## Quickstart
+
+```julia
+julia> using Finch
+
+# Create a sparse tensor
+julia> A = Tensor(CSCFormat(), [1 0 0; 0 2 0; 0 0 3])
+3×3 Tensor{DenseLevel{Int64, SparseListLevel{Int64, Vector{Int64}, Vector{Int64}, ElementLevel{0.0, Float64, Int64, Vector{Float64}}}}}:
+ 1.0  0.0  0.0
+ 0.0  2.0  0.0
+ 0.0  0.0  3.0
+
+# Perform a simple operation
+julia> B = A + A
+3×3 Tensor{DenseLevel{Int64, SparseDictLevel{Int64, Vector{Int64}, Vector{Int64}, Vector{Int64}, Dict{Tuple{Int64, Int64}, Int64}, Vector{Int64}, ElementLevel{0.0, Float64, Int64, Vector{Float64}}}}}:
+ 2.0  0.0  0.0
+ 0.0  4.0  0.0
+ 0.0  0.0  6.0
+```
+
+### How it Works
+Finch first translates high-level array code into **FinchLogic**, a custom intermediate representation that captures operator fusion and enables loop ordering optimizations. Using advanced schedulers, Finch optimizes FinchLogic and lowers it to **FinchNotation**, a more refined representation that precisely defines control flow. This optimized FinchNotation is then compiled into highly efficient, sparsity-aware code. Finch can specialize to each combination of sparse formats and algebraic properties, such as `x * 0 => 0`, eliminating unnecessary computations in sparse code automatically. 
+
+### Sparse and Structured Tensors
+
+Finch supports most major sparse formats (CSR, CSC, DCSR, DCSC, CSF, COO, Hash, Bytemap). Finch also allows users to define their own sparse formats with a parameterized format language.
+
+```
+CSC_matrix = Tensor(CSCFormat())
+CSR_matrix = swizzle(Tensor(CSCFormat()), 2, 1)
+CSF_tensor = Tensor(CSFFormat(3))
+```
+
+Finch also supports a wide variety of array structure beyond sparsity. Whether you're dealing with [custom background (zero) values](https://en.wikipedia.org/wiki/GraphBLAS), [run-length encoding](https://en.wikipedia.org/wiki/Run-length_encoding), or matrices with [special structures](https://en.wikipedia.org/wiki/Sparse_matrix#Special_structure) like banded or triangular matrices, Finch’s compiler can understand and optimize various data patterns and computational rules to adapt to the structure of data.
+
+### Examples:
+
+Finch supports many high-level array operations out of the box, such as `+`, `*`, `maximum`, `sum`, `map`, `broadcast`, and `reduce`.
+
+```julia
+julia> using Finch
+
+# Define sparse tensor A
+julia> A = Tensor(Dense(SparseList(Element(0.0))), [0 1.1 0; 2.2 0 3.3; 4.4 0 0; 0 0 5.5])
+
+# Define sparse tensor B
+julia> B = Tensor(Dense(SparseList(Element(0.0))), [0 1 1; 1 0 0; 0 0 1; 0 0 1])
+
+# Element-wise multiplication
+julia> C = A .* B
+
+# Element-wise max
+julia> C = max.(A, B)
+
+# Sum over rows
+julia> D = sum(C, dims=2)
+```
+
+For situations where more complex operations are needed, Finch supports an `@einsum` syntax on sparse and structured tensors.
+```julia
+julia> @einsum E[i] += A[i, j] * B[i, j]
+
+julia> @einsum F[i, k] <<max>>= A[i, j] + B[j, k]
+
+```
+
+Finch even allows users to fuse multiple operations into a single kernel with `lazy` and `compute`.  The `lazy` function creates a lazy tensor, which is a symbolic representation of the computation. The `compute` function evaluates the computation.
+Different optimizers can be used with `compute`, such as the state-of-the-art `Galley` optimizer, which can adapt to the
+sparsity patterns of the inputs.
+
+```julia
+julia> using Finch, BenchmarkTools
+
+julia> A = fsprand(1000, 1000, 0.1); B = fsprand(1000, 1000, 0.1); C = fsprand(1000, 1000, 0.0001);
+
+julia> A = lazy(A); B = lazy(B); C = lazy(C);
+
+julia> sum(A * B * C)
+
+julia> @btime compute(sum(A * B * C));
+  263.612 ms (1012 allocations: 185.08 MiB)
+
+julia> @btime compute(sum(A * B * C), ctx=galley_scheduler());
+  153.708 μs (667 allocations: 29.02 KiB)
+```
+
+## Learn More
+
+The following manuscripts provide a good description of the research behind Finch:
+
+[Finch: Sparse and Structured Array Programming with Control Flow](https://arxiv.org/abs/2404.16730).
+Willow Ahrens, Teodoro Fields Collin, Radha Patel, Kyle Deeds, Changwan Hong, Saman Amarasinghe.
+
+[Looplets: A Language for Structured Coiteration](https://doi.org/10.1145/3579990.3580020). CGO 2023. 
+Willow Ahrens, Daniel Donenfeld, Fredrik Kjolstad, Saman Amarasinghe.
+
+## Beyond Finch
+
+The following research efforts use Finch:
+
+[SySTeC: A Symmetric Sparse Tensor Compiler](https://arxiv.org/abs/2406.09266).
+Radha Patel, Willow Ahrens, Saman Amarasinghe.
+
+[The Continuous Tensor Abstraction: Where Indices are Real](https://arxiv.org/abs/2407.01742).
+Jaeyeon Won, Willow Ahrens, Joel S. Emer, Saman Amarasinghe.
+
+[Galley: Modern Query Optimization for Sparse Tensor Programs](https://arxiv.org/abs/2408.14706). [Galley.jl](https://github.com/kylebd99/Galley.jl).
+Kyle Deeds, Willow Ahrens, Magda Balazinska, Dan Suciu.
+
+## Contributing
+
+Contributions are welcome! Please see our [contribution guidelines](CONTRIBUTING.md) for more information.
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