CUTLASS 2.5.0

CUTLASS 2.5 is a minor release contributing:

• Tensor reductions
  • m-to-n reductions of tensors with affine layout
  • Specializations for reductions including contiguous dimension
  • Specializations for reductions excluding contiguous dimension
  • Custom reduction functors such as cutlass::logical_and
  • Large tensor support, up to 2^63 elements (however, each dimension is limited to an extent of 2^31)
• Optimizations for 3-D convolution
  • Optimized tile iterators using precomputed delta table for 3-D convolution
  • Full coverage of forward and backwards passes for 3D convolution
• Fused Convolution+Convolution example
• Corrections and bug fixes reported by the CUTLASS community
  • Thank you for filing these issues!

CUTLASS 2.4.0

CUTLASS 2.4

• Implicit GEMM convolution kernels supporting CUDA and Tensor Cores on NVIDIA GPUs
  • Operators: forward (Fprop), backward data gradient (Dgrad), and backward weight gradient (Wgrad) convolution
  • Data type: FP32, complex, Tensor Float 32 (TF32), BFloat16 (BF16), Float16, Int4, Int8, Int32
  • Spatial dimensions: 1-D, 2-D, and 3-D
  • Layout: NHWC, NCxHWx
• Implicit GEMM convolution components:
  • Global memory iterators supporting Fprop, Dgrad, and Wgrad
  • MmaMultistage for implicit GEMM convolution for NVIDIA Ampere architecture
  • MmaPipeline for implicit GEMM convolution for NVIDIA Volta and Turing architectures
  • Documentation describing Implicit GEMM Convolution algorithm and implementation

CUTLASS 2.3

CUTLASS 2.3

• NVIDIA Ampere Architecture features
  • Sparse Tensor Core GEMM kernels:
    • Direct access to Sparse Tensor Cores and maximum performance via mma.sp.sync
  • Fast SGEMM targeting GeForce RTX 30-series CUDA Cores
• Minor Features:
  • Activation functions such as GeLU and Sigmoid
  • Small matrix and quaternion template classes in device code
  • Floating-point constants
• NVIDIA Ampere GPU Architecture examples and documentation:
  • Tensor Float 32 and
  • Sparse Tensor Cores
  • Documentation added on CUTLASS efficient row-major epilogue

CUTLASS 2.2

• NVIDIA Ampere Architecture features
  • Fast Tensor Core operations:
  • Maximum performance via mma.sync
  • Tensor Float 32, BFloat16, and double-precision data types
  • Mixed integer data types (int8, int4, bin1)
  • Asynchronous copy for deep software pipelines via cp.async
  • Described in GTC 2020 Webinar (SR 21745) (free registration required)
• Features:
  • SDK examples showing GEMM fused with bias+relu and fused GEMM+GEMM
  • Complex-valued GEMMs targeting NVIDIA Ampere Tensor Cores in double-precision and Tensor Float 32
  • Gaussian complex GEMMs using 3m complex multiply algorithm
  • Universal GEMM kernel supporting two batch modes and two algorithms for parallel reductions
• Policy updates:
  • CUDA 11 Toolkit needed to enable NVIDIA Ampere Architecture features
  • Disabled F16C by default for compatibility - enable on cmake command line with -DCUTLASS_ENABLE_F16C=ON

CUTLASS 2.1

Planar Complex GEMM kernels targeting Volta and Turing Tensor Cores

• Computes complex matrix products on matrices stored as disjoint real and imaginary parts
• SDK Examples of Planar Complex GEMMs

BLAS-style host-side API added to CUTLASS Library

• API to launch compiled kernel instances for GEMM and planar complex GEMM

Minor enhancements and bug fixes

CUTLASS 2.0

Substantially refactored for

• Better performance, particularly for native Turing Tensor Cores
• Robust and durable templates spanning the design space
• Encapsulated functionality embodying modern C++11 programming techniques
• Optimized containers and data types for efficient, generic, portable device code

Updates to:

• Quick start guide
• Documentation
• Utilities
• CUTLASS Profiler

Native Turing Tensor Cores

• Efficient GEMM kernels targeting Turing Tensor Cores
• Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands

Coverage of existing CUTLASS functionality

• GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs
• Volta Tensor Cores through native mma.sync and through WMMA API
• Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions
• Batched GEMM operations
• Complex-valued GEMMs

Note: a host compiler supporting C++11 or greater is required.

CUTLASS 1.3.3

Final tagged release of CUTLASS 1.x branch.

CUTLASS 1.3.2

Performance enhancement for Volta Tensor Cores TN layout

• Fixed performance defect with indirect access to pointer array for Volta TensorCores TN arrangement.

CUTLASS 1.3.0

CUTLASS 1.3 adds efficient GEMM kernels targeting Volta Tensor Cores via mma.sync instruction added in CUDA 10.1.

CUTLASS 1.2

CUTLASS 1.2.0
(2018-10-26)

• Parallelized reductions across threadblocks ("Split-K")
• Improved IGEMM performance
• Batched strided WMMA GEMMs