CUDA.jl 2.0

Re-posted from: https://juliagpu.org/2020-10-02-cuda_2.0/

Today we’re releasing CUDA.jl 2.0, a breaking release with several new features. Highlights
include initial support for Float16, a switch to CUDA’s new stream model, a much-needed
rework of the sparse array support and support for CUDA 11.1.

The release now requires Julia 1.5, and assumes a GPU with compute capability 5.0 or
higher (although most of the package will still work with an older GPU).

Low- and mixed-precision operations

With NVIDIA’s latest GPUs emphasizing support for low-precision operations, CUDA.jl
now starts to support. For example,
the CUBLAS wrappers can be used with (B)Float16 inputs (running under JULIA_DEBUG=CUBLAS
to illustrate the called methods) thanks to the cublasGemmEx API call:

julia> mul!(CUDA.zeros(Float32,2,2), cu(rand(Float16,2,2)), cu(rand(Float16,2,2)))

I! cuBLAS (v11.0) function cublasStatus_t cublasGemmEx(...) called:
i!  Atype: type=cudaDataType_t; val=CUDA_R_16F(2)
i!  Btype: type=cudaDataType_t; val=CUDA_R_16F(2)
i!  Ctype: type=cudaDataType_t; val=CUDA_R_32F(0)
i!  computeType: type=cublasComputeType_t; val=CUBLAS_COMPUTE_32F(68)

2×2 CuArray{Float32,2}:
 0.481284  0.561241
 1.12923   1.04541

julia> using BFloat16s

julia> mul!(CUDA.zeros(BFloat16,2,2), cu(BFloat16.(rand(2,2))), cu(BFloat16.(rand(2,2))))

I! cuBLAS (v11.0) function cublasStatus_t cublasGemmEx(...) called:
i!  Atype: type=cudaDataType_t; val=CUDA_R_16BF(14)
i!  Btype: type=cudaDataType_t; val=CUDA_R_16BF(14)
i!  Ctype: type=cudaDataType_t; val=CUDA_R_16BF(14)
i!  computeType: type=cublasComputeType_t; val=CUBLAS_COMPUTE_32F(68)

2×2 CuArray{BFloat16,2}:
 0.300781   0.71875
 0.0163574  0.0241699

Alternatively, CUBLAS can be configured to automatically down-cast 32-bit inputs to Float16.
This is now exposed through a task-local
CUDA.jl math mode:

julia> CUDA.math_mode!(CUDA.FAST_MATH; precision=:Float16)

julia> mul!(CuArray(zeros(Float32,2,2)), CuArray(rand(Float32,2,2)), CuArray(rand(Float32,2,2)))

I! cuBLAS (v11.0) function cublasStatus_t cublasGemmEx(...) called:
i!  Atype: type=cudaDataType_t; val=CUDA_R_32F(0)
i!  Btype: type=cudaDataType_t; val=CUDA_R_32F(0)
i!  Ctype: type=cudaDataType_t; val=CUDA_R_32F(0)
i!  computeType: type=cublasComputeType_t; val=CUBLAS_COMPUTE_32F_FAST_16F(74)

2×2 CuArray{Float32,2}:
 0.175258  0.226159
 0.511893  0.331351

As part of these changes, CUDA.jl now defaults to using tensor cores. This may affect
accuracy; use math mode PEDANTIC if you want the old behavior.

Work is under way to extend these
capabilities to the rest of CUDA.jl, e.g., the CUDNN wrappers, or the native kernel
programming capabilities.

New default stream semantics

In CUDA.jl 2.0 we’re switching to CUDA’s
simplified stream programming
model.
This simplifies working with multiple streams, and opens up more possibilities for
concurrent execution of GPU operations.

Multi-stream programming

In the old model, the default stream (used by all GPU operations unless specified otherwise)
was a special stream whose commands could not be executed concurrently with commands on
regular, explicitly-created streams. For example, if we interleave kernels executed on a
dedicated stream with ones on the default one, execution was serialized:

using CUDA

N = 1 << 20

function kernel(x, n)
    tid = threadIdx().x + (blockIdx().x-1) * blockDim().x
    for i = tid:blockDim().x*gridDim().x:n
        x[i] = CUDA.sqrt(CUDA.pow(3.14159f0, i))
    end
    return
end

num_streams = 8

for i in 1:num_streams
    stream = CuStream()

    data = CuArray{Float32}(undef, N)

    @cuda blocks=1 threads=64 stream=stream kernel(data, N)

    @cuda kernel(data, 0)
end

In the new model, default streams are regular streams and commands issued on them can
execute concurrently with those on other streams:

Multi-threading

Another consequence of the new stream model is that each thread gets its own default stream
(accessible as CuStreamPerThread()). Together with Julia’s treading capabilities, this
makes it trivial to group independent work in tasks, benefiting from concurrent execution on
the GPU where possible:

using CUDA

N = 1 << 20

function kernel(x, n)
    tid = threadIdx().x + (blockIdx().x-1) * blockDim().x
    for i = tid:blockDim().x*gridDim().x:n
        x[i] = CUDA.sqrt(CUDA.pow(3.14159f0, i))
    end
    return
end

Threads.@threads for i in 1:Threads.nthreads()
    data = CuArray{Float32}(undef, N)
    @cuda blocks=1 threads=64 kernel(data, N)
    synchronize(CuDefaultStream())
end

With the old model, execution would have been serialized because the default stream was the
same across threads:

Future improvements will make this behavior configurable, such that users can use a
different default stream per task.

Sparse array clean-up

As part of CUDA.jl 2.0, the sparse array support has been
refactored, bringing them in line with other
array types and their expected behavior. For example, the custom switch2 methods have been
removed in favor of calls to convert and array constructors:

julia> using SparseArrays
julia> using CUDA, CUDA.CUSPARSE

julia> CuSparseMatrixCSC(CUDA.rand(2,2))
2×2 CuSparseMatrixCSC{Float32} with 4 stored entries:
  [1, 1]  =  0.124012
  [2, 1]  =  0.791714
  [1, 2]  =  0.487905
  [2, 2]  =  0.752466

julia> CuSparseMatrixCOO(sprand(2,2, 0.5))
2×2 CuSparseMatrixCOO{Float64} with 3 stored entries:
  [1, 1]  =  0.183183
  [2, 1]  =  0.966466
  [2, 2]  =  0.064101

julia> CuSparseMatrixCSR(ans)
2×2 CuSparseMatrixCSR{Float64} with 3 stored entries:
  [1, 1]  =  0.183183
  [2, 1]  =  0.966466
  [2, 2]  =  0.064101

Initial support for the COO sparse matrix type
has also been added, along with more better
support for sparse matrix-vector
multiplication.

Support for CUDA 11.1

This release also features support for the brand-new CUDA 11.1. As there is no compatible
release of CUDNN or CUTENSOR yet, CUDA.jl won’t automatically select this version, but you
can force it to by setting the JULIA_CUDA_VERSION environment variable to 11.1:

julia> ENV["JULIA_CUDA_VERSION"] = "11.1"

julia> using CUDA

julia> CUDA.versioninfo()
CUDA toolkit 11.1.0, artifact installation

Libraries:
- CUDNN: missing
- CUTENSOR: missing

Minor changes

Many other changes are part of this release:

Views, reshapes and array reinterpretations are now
represented by the Base array wrappers,
simplifying the CuArray type definition.
Various optimizations to CUFFT and
CUDNN library wrappers.
Support for LinearAlgebra.reflect! and
rotate!
Initial support for calling CUDA libraries
with strided inputs

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