NVIDIA CUDA Toolkit Release Notes
---------------------------------
The Release Notes for the CUDA Toolkit.
1. CUDA 11.5 Release Notes
--------------------------
The release notes for the NVIDIA® CUDA® Toolkit can be found
online at
http://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html.
Note: The release notes have been reorganized into two major
sections: the general CUDA release notes, and the CUDA
libraries release notes including historical information for
11.x releases.
1.1. CUDA Toolkit Major Component Versions
CUDA Components
Starting with CUDA 11, the various components in the
toolkit are versioned independently.
For CUDA 11.5, the table below indicates the versions:
Table 1. CUDA 11.5 Component Versions
Component Name
Version Information
Supported Architectures
CUDA Runtime (cudart)
11.5.50
x86_64, POWER, Arm64
cuobjdump
11.5.50
x86_64, POWER, Arm64
CUPTI
11.5.57
x86_64, POWER, Arm64
CUDA cuxxfilt (demangler)
11.5.50
x86_64, POWER, Arm64
CUDA Demo Suite
11.5.50
x86_64
CUDA GDB
11.5.50
x86_64, POWER, Arm64
CUDA Memcheck
11.5.50
x86_64, POWER
CUDA NVCC
11.5.50
x86_64, POWER, Arm64
CUDA nvdisasm
11.5.50
x86_64, POWER, Arm64
CUDA NVML Headers
11.5.50
x86_64, POWER, Arm64
CUDA nvprof
11.5.50
x86_64, POWER, Arm64
CUDA nvprune
11.5.50
x86_64, POWER, Arm64
CUDA NVRTC
11.5.50
x86_64, POWER, Arm64
CUDA NVTX
11.5.50
x86_64, POWER, Arm64
CUDA NVVP
11.5.50
x86_64, POWER
CUDA Samples
11.5.56
x86_64, POWER, Arm64
CUDA Compute Sanitizer API
11.5.50
x86_64, POWER, Arm64
CUDA Thrust
11.5.62
x86_64, POWER, Arm64
CUDA cuBLAS
11.7.3.1
x86_64, POWER, Arm64
CUDA cuFFT
10.6.0.54
x86_64, POWER, Arm64
CUDA cuFile
1.1.0.37
x86_64
CUDA cuRAND
10.2.6.48
x86_64, POWER, Arm64
CUDA cuSOLVER
11.2.1.48
x86_64, POWER, Arm64
CUDA cuSPARSE
11.7.0.31
x86_64, POWER, Arm64
CUDA NPP
11.5.1.53
x86_64, POWER, Arm64
CUDA nvJPEG
11.5.3.48
x86_64, POWER, Arm64
Nsight Compute
2021.3.0.13
x86_64, POWER, Arm64 (CLI only)
NVTX
1.21018621
x86_64, POWER, Arm64
Nsight Systems
2021.3.3.2
x86_64, POWER, Arm64 (CLI only)
Nsight Visual Studio Edition (VSE)
2021.3.0.21265
x86_64 (Windows)
nvidia_fs1
2.8.2
x86_64
Visual Studio Integration
11.5.50
x86_64 (Windows)
NVIDIA Linux Driver
495.29.05
x86_64, POWER, Arm64
NVIDIA Windows Driver
496.04
x86_64 (Windows)
CUDA Driver
Running a CUDA application requires the system with at
least one CUDA capable GPU and a driver that is
compatible with the CUDA Toolkit. See Table 3. For more
information various GPU products that are CUDA capable,
visit https://developer.nvidia.com/cuda-gpus.
Each release of the CUDA Toolkit requires a minimum
version of the CUDA driver. The CUDA driver is backward
compatible, meaning that applications compiled against a
particular version of the CUDA will continue to work on
subsequent (later) driver releases.
More information on compatibility can be found at
https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html#cuda-compatibility-and-upgrades.
Note: Starting with CUDA 11.0, the toolkit components
are individually versioned, and the toolkit itself is
versioned as shown in the table below.
The minimum required driver version for CUDA enhanced
compatibility is shown below. CUDA Enhanced
Compatibility is described in detail in
https://docs.nvidia.com/deploy/cuda-compatibility/index.html
Table 2. CUDA Toolkit and Minimum Required Driver
Version for CUDA Enhanced Compatibility
CUDA Toolkit
Minimum Required Driver Version for CUDA Enhanced
Compatibility*
Linux x86_64 Driver Version
Windows x86_64 Driver Version
CUDA 11.5.x
>=450.80.02
>=456.38
CUDA 11.4.x
>=450.80.02
>=456.38
CUDA 11.3.x
>=450.80.02
>=456.38
CUDA 11.2.x
>=450.80.02
>=456.38
CUDA 11.1 (11.1.0)
>=450.80.02
>=456.38
CUDA 11.0 (11.0.3)
>=450.36.06**
>=456.38
* Using a Minimum Required Version that is different
from Toolkit Driver Version could be allowed in
compatibility mode -- please read the CUDA Compatibility
Guide for details.
** CUDA 11.0 was released with an earlier driver
version, but by upgrading to 450.80.02 driver as
indicated, minor version compatibility is possible
across the CUDA 11.x family of toolkits.
The version of the development NVIDIA GPU Driver
packaged in each CUDA Toolkit release is shown below.
Table 3. CUDA Toolkit and Corresponding Driver Versions
CUDA Toolkit
Toolkit Driver Version
Linux x86_64 Driver Version
Windows x86_64 Driver Version
CUDA 11.5 GA
>=495.29.05
>=496.04
CUDA 11.4 Update 2
>=470.57.02
>=471.41
CUDA 11.4 Update 1
>=470.57.02
>=471.41
CUDA 11.4.0 GA
>=470.42.01
>=471.11
CUDA 11.3.1 Update 1
>=465.19.01
>=465.89
CUDA 11.3.0 GA
>=465.19.01
>=465.89
CUDA 11.2.2 Update 2
>=460.32.03
>=461.33
CUDA 11.2.1 Update 1
>=460.32.03
>=461.09
CUDA 11.2.0 GA
>=460.27.03
>=460.82
CUDA 11.1.1 Update 1
>=455.32
>=456.81
CUDA 11.1 GA
>=455.23
>=456.38
CUDA 11.0.3 Update 1
>= 450.51.06
>= 451.82
CUDA 11.0.2 GA
>= 450.51.05
>= 451.48
CUDA 11.0.1 RC
>= 450.36.06
>= 451.22
CUDA 10.2.89
>= 440.33
>= 441.22
CUDA 10.1 (10.1.105 general release, and updates)
>= 418.39
>= 418.96
CUDA 10.0.130
>= 410.48
>= 411.31
CUDA 9.2 (9.2.148 Update 1)
>= 396.37
>= 398.26
CUDA 9.2 (9.2.88)
>= 396.26
>= 397.44
CUDA 9.1 (9.1.85)
>= 390.46
>= 391.29
CUDA 9.0 (9.0.76)
>= 384.81
>= 385.54
CUDA 8.0 (8.0.61 GA2)
>= 375.26
>= 376.51
CUDA 8.0 (8.0.44)
>= 367.48
>= 369.30
CUDA 7.5 (7.5.16)
>= 352.31
>= 353.66
CUDA 7.0 (7.0.28)
>= 346.46
>= 347.62
For convenience, the NVIDIA driver is installed as part
of the CUDA Toolkit installation. Note that this driver
is for development purposes and is not recommended for
use in production with Tesla GPUs.
For running CUDA applications in production with Tesla
GPUs, it is recommended to download the latest driver
for Tesla GPUs from the NVIDIA driver downloads site at
http://www.nvidia.com/drivers.
During the installation of the CUDA Toolkit, the
installation of the NVIDIA driver may be skipped on
Windows (when using the interactive or silent
installation) or on Linux (by using meta packages).
For more information on customizing the install process
on Windows, see
http://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html#install-cuda-software.
For meta packages on Linux, see
https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#package-manager-metas
1.2. General CUDA
11.5
* Device-side caching behavior is now configurable with
annotated pointers.
* Prefix sums (scans) for cooperative groups: Added four new
functions for inclusive and exclusive scans.
* Support for NvSciBufGeneralAttrKey_EnableGpuCompression in
CUDA will be available in r470TRD2.
* Preview release of a new data type, __int128, usable with
compatible host compilers. As it is a preview, there is no
broad support for math operations, library support, dev
tools, and so on.
* Added native support for signed and unsigned normalized 8-
and 16-bit types.
* Improved interoperability with graphics frameworks: Added
support for normalized integer and block-compressed data
types.
* For multi-process sharing of GPUs, CUDA now supports
per-process memory access policies.
* Linking is supported with cubins larger than 2 GB.
* GSP-RM is enabled as opt-in for Turing+ Tesla GPUs.
* Floating point division is optimized when the divisor is
known at compile time. This is disabled by default; enable
with nvcc -Xcicc -opt-fdiv=1
* GA release of CUDA Python.
1.3. CUDA Compilers
* CUDA now provides a static version of the NVRTC library.
* builtin_assume can now be used to specify address space to
allow for efficient loads and stores.
* Added support for numerous pragmas that offer more control
over diagnostic messages.
* To generate code for multiple architectures at the same
time, use the -arch=all or -arch=all-major options.
* Deterministic code generation: compiled bits will not
change between nvcc invocations without semantic source
code changes.
1.4. Resolved Issues
1.4.1. CUDA Compilers
* Linking with cubins larger than 2 GB is now supported.
* Certain C++17 features that were backported to C++14 in
MSVC are now be supported.
* A user reported issue with the use of lambda function when
an object is passed-by-value is resolved.
https://github.com/Ahdhn/nvcc_bug_maybe
1.5. Deprecated Features
The following features are deprecated in the current release
of the CUDA software. The features still work in the current
release, but their documentation may have been removed, and
they will become officially unsupported in a future release.
We recommend that developers employ alternative solutions to
these features in their software.
General CUDA
* NVIDIA Driver support for Kepler is removed
beginning with R495. CUDA Toolkit development
support for Kepler continues through CUDA 11.x.
1.6. Known Issues
1.6.1. General CUDA
* Intermittent crashes were seen when CUDA binaries were
running on a system with a GLIBC version older than
2.17-106.el7_2.1. This is due to a known bug in older
versions of GLIBC (Bug reference:
https://bugzilla.redhat.com/show_bug.cgi?id=1293976) and
has been fixed in later versions (>= glibc-2.17-107.el7).
2. CUDA Libraries
-----------------
This section covers CUDA Libraries release notes for 11.x
releases.
* CUDA Math Libraries toolchain uses C++11 features, and a
C++11-compatible standard library (libstdc++ >= 20150422)
is required on the host.
* CUDA Math libraries are no longer shipped for SM30 and
SM32.
* Support for the following compute capabilities are
deprecated for all libraries:
* sm_35 (Kepler)
* sm_37 (Kepler)
* sm_50 (Maxwell)
2.1. cuBLAS Library
2.1.1. cuBLAS: Release 11.4 Update 2
* New Features
* Vector (and batched) alpha support for per-row scaling
in TN int32 math Matmul with int8 output. See
CUBLASLT_POINTER_MODE_ALPHA_DEVICE_VECTOR_BETA_HOST
and CUBLASLT_MATMUL_DESC_ALPHA_VECTOR_BATCH_STRIDE.
* New epilogue options have been added to support fusion
in DLtraining: CUBLASLT_EPILOGUE_BGRADA and
CUBLASLT_EPILOGUE_BGRADB which compute bias gradients
based on matrices A and B respectively.
* New auxiliary functions cublasGetStatusName(),
cublasGetStatusString() have been added to cuBLAS that
return the string representation and the description
of the cuBLAS status (cublasStatus_t) respectively.
Similarly, cublasLtGetStatusName(),
cublasLtGetStatusString() have been added to cuBlasLt.
* Known Issues
* cublasGemmBatchedEx() and cublas<t>gemmBatched() check
the alignment of the input/output arrays of the
pointers like they were the pointers to the actual
matrices. These checks are irrelevant and will be
disabled in future releases. This mostly affects
half-precision inputGEMMs which might require 16-byte
alignment, and array of pointers could only be aligned
by 8-byte boundary.
* Resolved Issues
* cublasLtMatrixTransform can now operate on matrices
with dimensions greater than 65535.
* Fixed out-of-bound access in GEMM and Matmul
functions, when split K or non-default epilogue is
used and leading dimension of the output matrix
exceeds int32_t limit.
* NVBLAS now uses lazy loading of the CPU BLAS library
on Linux to avoid issues caused by preloading
libnvblas.so in complex applications that use fork and
similar APIs.
* Resolved symbols name conflict when using cuBlasLt
static library with static TensorRT or cuDNN
libraries.
2.1.2. cuBLAS: Release 11.4
* Resolved Issues
* Some gemv cases were producing incorrect results if
the matrix dimension (n or m) was large, for example
2^20.
2.1.3. cuBLAS: Release 11.3 Update 1
* New Features
* Some new kernels have been added for improved
performance but have the limitation that only host
pointers are supported for scalars (for example, alpha
and beta parameters). This limitation is expected to
be resolved in a future release.
* New epilogues have been added to support fusion in ML
training. These include:
* ReLuBias and GeluBias epilogues that produce an
auxiliary output which is used on backward
propagation to compute the corresponding
gradients.
* DReLuBGrad and DGeluBGrad epilogues that compute
the backpropagation of the corresponding
activation function on matrix C, and produce bias
gradient as a separate output. These epilogues
require auxiliary input mentioned in the bullet
above.
* Resolved Issues
* Some tensor core accelerated strided batched GEMM
routines would result in misaligned memory access
exceptions when batch stride wasn't a multiple of 8.
* Tensor core accelerated cublasGemmBatchedEx
(pointer-array) routines would use slower variants of
kernels assuming bad alignment of the pointers in the
pointer array. Now it assumes that pointers are well
aligned, as noted in the documentation.
* Known Issues
* To be able to access the fastest possible kernels
through cublasLtMatmulAlgoGetHeuristic() you need to
set CUBLASLT_MATMUL_PREF_POINTER_MODE_MASK in search
preferences to CUBLASLT_POINTER_MODE_MASK_HOST or
CUBLASLT_POINTER_MODE_MASK_NO_FILTERING. By default,
heuristics query assumes the pointer mode may change
later and only returns algo configurations that
support both _HOST and _DEVICE modes. Without this,
newly added kernels will be excluded and it will
likely lead to a performance penalty on some problem
sizes.
* Deprecated Features
* Linking with static cublas and cublasLt libraries on
Linux now requires using gcc-5.2 and compatible or
higher due to C++11 requirements in these libraries.
2.1.4. cuBLAS: Release 11.3
* Known Issues
* The planar complex matrix descriptor for batched
matmul has inconsistent interpretation of batch
offset.
* Mixed precision operations with reduction scheme
CUBLASLT_REDUCTION_SCHEME_OUTPUT_TYPE (might be
automatically selected based on problem size by
cublasSgemmEx() or cublasGemmEx() too, unless
CUBLAS_MATH_DISALLOW_REDUCED_PRECISION_REDUCTION math
mode bit is set) not only stores intermediate results
in output type but also accumulates them internally in
the same precision, which may result in lower than
expected accuracy. Please use
CUBLASLT_MATMUL_PREF_REDUCTION_SCHEME_MASK or
CUBLAS_MATH_DISALLOW_REDUCED_PRECISION_REDUCTION if
this results in numerical precision issues in your
application.
2.1.5. cuBLAS: Release 11.2
* Known Issues
* cublas<s/d/c/z>Gemm() with very large n and m=k=1 may
fail on Pascal devices.
2.1.6. cuBLAS: Release 11.1 Update 1
* New Features
* cuBLASLt Logging is officially stable and no longer
experimental. cuBLASLt Logging APIs are still
experimental and may change in future releases.
* Resolved Issues
* cublasLt Matmul fails on Volta architecture GPUs with
CUBLAS_STATUS_EXECUTION_FAILED when n dimension >
262,137 and epilogue bias feature is being used. This
issue exists in 11.0 and 11.1 releases but has been
corrected in 11.1 Update 1
2.1.7. cuBLAS: Release 11.1
* Resolved Issues
* A performance regression in the cublasCgetrfBatched
and cublasCgetriBatched routines has been fixed.
* The IMMA kernels do not support padding in matrix C
and may corrupt the data when matrix C with padding is
supplied to cublasLtMatmul. A suggested work around is
to supply matrix C with leading dimension equal to 32
times the number of rows when targeting the IMMA
kernels: computeType = CUDA_R_32I and
CUBLASLT_ORDER_COL32 for matrices A,C,D, and
CUBLASLT_ORDER_COL4_4R2_8C (on NVIDIA Ampere GPU
architecture or Turing architecture) or
CUBLASLT_ORDER_COL32_2R_4R4 (on NVIDIA Ampere GPU
architecture) for matrix B. Matmul descriptor must
specify CUBLAS_OP_T on matrix B and CUBLAS_OP_N
(default) on matrix A and C. The data corruption
behavior was fixed so that CUBLAS_STATUS_NOT_SUPPORTED
is returned instead.
* Fixed an issue that caused an Address out of bounds
error when calling cublasSgemm().
* A performance regression in the cublasCgetrfBatched
and cublasCgetriBatched routines has been fixed.
2.1.8. cuBLAS: Release 11.0 Update 1
* New Features
* The cuBLAS API was extended with a new function,
cublasSetWorkspace(), which allows the user to set the
cuBLAS library workspace to a user-owned device
buffer, which will be used by cuBLAS to execute all
subsequent calls to the library on the currently set
stream.
* cuBLASLt experimental logging mechanism can be enabled
in two ways:
* By setting the following environment variables
before launching the target application:
* CUBLASLT_LOG_LEVEL=<level> -- where level is
one of the following levels:
* "0" - Off - logging is disabled (default)
* "1" - Error - only errors will be logged
* "2" - Trace - API calls that launch CUDA
kernels will log their parameters and
important information
* "3" - Hints - hints that can potentially
improve the application's performance
* "4" - Heuristics - heuristics log that may
help users to tune their parameters
* "5" - API Trace - API calls will log their
parameter and important information
* CUBLASLT_LOG_MASK=<mask> -- where mask is a
combination of the following masks:
* "0" - Off
* "1" - Error
* "2" - Trace
* "4" - Hints
* "8" - Heuristics
* "16" - API Trace
* CUBLASLT_LOG_FILE=<value> -- where value is a
file name in the format of "<file_name>.%i",
%i will be replaced with process id.If
CUBLASLT_LOG_FILE is not defined, the log
messages are printed to stdout.
* By using the runtime API functions defined in the
cublasLt header:
* typedef void(*cublasLtLoggerCallback_t)(int
logLevel, const char* functionName, const
char* message) -- A type of callback function
pointer.
* cublasStatus_t
cublasLtLoggerSetCallback(cublasLtLoggerCallback_t
callback) -- Allows to set a call back
functions that will be called for every
message that is logged by the library.
* cublasStatus_t cublasLtLoggerSetFile(FILE*
file) -- Allows to set the output file for the
logger. The file must be open and have write
permissions.
* cublasStatus_t cublasLtLoggerOpenFile(const
char* logFile) -- Allows to give a path in
which the logger should create the log file.
* cublasStatus_t cublasLtLoggerSetLevel(int
level) -- Allows to set the log level to one
of the above mentioned levels.
* cublasStatus_t cublasLtLoggerSetMask(int mask)
-- Allows to set the log mask to a combination
of the above mentioned masks.
* cublasStatus_t cublasLtLoggerForceDisable() --
Allows to disable to logger for the entire
session. Once this API is being called, the
logger cannot be reactivated in the current
session.
2.1.9. cuBLAS: Release 11.0
* New Features
* cuBLASLt Matrix Multiplication adds support for fused
ReLU and bias operations for all floating point types
except double precision (FP64).
* Improved batched TRSM performance for matrices larger
than 256.
2.1.10. cuBLAS: Release 11.0 RC
* New Features
* Many performance improvements have been implemented
for NVIDIA Ampere, Volta, and Turing Architecture
based GPUs.
* The cuBLASLt logging mechanism can be enabled by
setting the following environment variables before
launching the target application:
* CUBLASLT_LOG_LEVEL=<level> - while level is one of
the following levels:
* "0" - Off - logging is disabled (default)
* "1" - Error - only errors will be logged
* "2" - Trace - API calls will be logged with
their parameters and important information
* CUBLASLT_LOG_FILE=<value> - while value is a file
name in the format of "<file_name>.%i", %i will be
replaced with process id. If CUBLASLT_LOG_FILE is
not defined, the log messages are printed to
stdout.
* For matrix multiplication APIs:
* cublasGemmEx, cublasGemmBatchedEx,
cublasGemmStridedBatchedEx and cublasLtMatmul
added new data type support for __nv_bfloat16
(CUDA_R_16BF).
* A new compute type TensorFloat32 (TF32) has been
added to provide tensor core acceleration for FP32
matrix multiplication routines with full dynamic
range and increased precision compared to
BFLOAT16.
* New compute modes Default, Pedantic, and Fast have
been introduced to offer more control over compute
precision used.
* Tensor cores are now enabled by default for half-,
and mixed-precision- matrix multiplications.
* Double precision tensor cores (DMMA) are used
automatically.
* Tensor cores can now be used for all sizes and
data alignments and for all GPU architectures:
* Selection of these kernels through cuBLAS
heuristics is automatic and will depend on
factors such as math mode setting as well as
whether it will run faster than the non-tensor
core kernels.
* Users should note that while these new kernels
that use tensor cores for all unaligned cases
are expected to perform faster than non-tensor
core based kernels but slower than kernels
that can be run when all buffers are well
aligned.
* Deprecated Features
* Algorithm selection in cublasGemmEx APIs (including
batched variants) is non-functional for NVIDIA Ampere
Architecture GPUs. Regardless of selection it will
default to a heuristics selection. Users are
encouraged to use the cublasLt APIs for algorithm
selection functionality.
* The matrix multiply math mode CUBLAS_TENSOR_OP_MATH is
being deprecated and will be removed in a future
release. Users are encouraged to use the new
cublasComputeType_t enumeration to define compute
precision.
2.2. cuFFT Library
2.2.1. cuFFT: Release 11.5
* Known Issues
* FFTs of certain sizes in single and double precision
(multiples of size 6) could fail on future devices.
This issue will be fixed in an upcoming release.
2.2.2. cuFFT: Release 11.4 Update 2
* Resolved Issues
* Since cuFFT 10.3.0 (CUDA Toolkit 11.1), cuFFT may
require user to make sure that all operations on input
and output buffers are complete before calling
cufft[Xt]Exec* if:
* sm70 or later, 3D FFT, batch > 1, total size of
transform is greater than 4.5MB
* FFT size for all dimensions is in the set of the
following sizes: {2, 4, 8, 16, 32, 64, 128, 3, 9,
81, 243, 729, 2187, 6561, 5, 25, 125, 625, 3125,
6, 36, 216, 1296, 7776, 7, 49, 343, 2401, 11, 121}
* Some V100 FFTs were slower than expected. This issue
is resolved.
* Known Issues
* Some T4 FFTs are slower than expected.
* Plans for FFTs of certain sizes in single precision
(including some multiples of 1024 sizes, and some
large prime numbers) could fail on future devices with
less than 64 kB of shared memory. This issue will be
fixed in an upcoming release.
2.2.3. cuFFT: Release 11.4 Update 1
* Resolved Issues
* Some cuFFT multi-GPU plans exhibited very long
creation times.
* cuFFT sometimes produced incorrect results for
real-to-complex and complex-to-real transforms when
the total number of elements across all batches in a
single execution exceeded 2147483647.
* Known Issues
* Some V100 FFTs are slower than expected.
* Some T4 FFTs are slower than expected.
2.2.4. cuFFT: Release 11.4
* New Features
* Performance improvements.
* Known Issues
* Some T4 FFTs are slower than expected.
* cuFFT may produce incorrect results for
real-to-complex and complex-to-real transforms when
the total number of elements across all batches in a
single execution exceeds 2147483647.
* Some cuFFT multi-GPU plans may exhibit very long
creation time. Issue will be fixed in the next update.
* cuFFT may produce incorrect results for transforms
with strides when the index of the last element across
all batches exceeds 2147483647 (see Advanced Data
Layout).
* Deprecated Features
* Support for callback functionality using separately
compiled device code is deprecated on all GPU
architectures. Callback functionality will continue to
be supported for all GPU architectures.
2.2.5. cuFFT: Release 11.3
* New Features
* cuFFT shared libraries are now linked statically
against libstdc++ on Linux platforms.
* Improved performance of certain sizes (multiples of
large powers of 3, powers of 11) in SM86.
* Known Issues
* cuFFT planning and plan estimation functions may not
restore correct context affecting CUDA driver API
applications.
* Plans with strides, primes larger than 127 in FFT size
decomposition and total size of transform including
strides bigger than 32GB produce incorrect results.
2.2.6. cuFFT: Release 11.2 Update 2
* Known Issues
* cuFFT planning and plan estimation functions may not
restore correct context affecting CUDA driver API
applications.
* Plans with strides, primes larger than 127 in FFT size
decomposition and total size of transform including
strides bigger than 32GB produce incorrect results.
2.2.7. cuFFT: Release 11.2 Update 1
* Resolved Issues
* Previously, reduced performance of power-of-2 single
precision FFTs was observed on GPUs with sm_86
architecture. This issue has been resolved.
* Large prime factors in size decomposition and real to
complex or complex to real FFT type no longer cause
cuFFT plan functions to fail.
* Known Issues
* cuFFT planning and plan estimation functions may not
restore correct context affecting CUDA driver API
applications.
* Plans with strides, primes larger than 127 in FFT size
decomposition and total size of transform including
strides bigger than 32GB produce incorrect results.
2.2.8. cuFFT: Release 11.2
* New Features
* Multi-GPU plans can be associated with a stream using
the cufftSetStream API function call.
* Performance improvements for R2C/C2C/C2R transforms.
* Performance improvements for multi-GPU systems.
* Resolved Issues
* cuFFT is no longer stuck in a bad state if previous
plan creation fails with CUFFT_ALLOC_FAILED.
* Previously, single dimensional multi-GPU FFT plans
ignored user input on cufftXtSetGPUswhichGPUs argument
and assumed that GPUs IDs are always numbered from 0
to N-1. This issue has been resolved.
* Plans with primes larger than 127 in FFT size
decomposition or FFT size being a prime number bigger
than 4093 do not perform calculations on second and
subsequent cufftExecute* calls. The regression was
introduced in cuFFT 11.1.
* Known Issues
* cuFFT planning and plan estimation functions may not
restore correct context affecting CUDA driver API
applications.
2.2.9. cuFFT: Release 11.1
* New Features
* cuFFT is now L2-cache aware and uses L2 cache for GPUs
with more than 4.5MB of L2 cache. Performance may
improve in certain single-GPU 3D C2C FFT cases.
* After successfully creating a plan, cuFFT now enforces
a lock on the cufftHandle. Subsequent calls to any
planning function with the same cufftHandle will fail.
* Added support for very large sizes (3k cube) to
multi-GPU cuFFT on DGX-2.
* Improved performance on multi-gpu cuFFT for certain
sizes (1k cube).
* Resolved Issues
* Resolved an issue that caused cuFFT to crash when
reusing a handle after clearing a callback.
* Fixed an error which produced incorrect results / NaN
values when running a real-to-complex FFT in half
precision.
* Known Issues
* cuFFT will always overwrite the input for out-of-place
C2R transform.
* Single dimensional multi-GPU FFT plans ignore user
input on the whichGPUs parameter of cufftXtSetGPUs()
and assume that GPUs IDs are always numbered from 0 to
N-1.
2.2.10. cuFFT: Release 11.0 RC
* New Features
* cuFFT now accepts __nv_bfloat16 input and output data
type for power-of-two sizes with single precision
computations within the kernels.
* Reoptimized power of 2 FFT kernels on Volta and Turing
architectures.
* Resolved Issues
* Reduced R2C/C2R plan memory usage to previous levels.
* Resolved bug introduced in 10.1 update 1 that caused
incorrect results when using custom strides, batched
2D plans and certain sizes on Volta and later.
* Known Issues
* cuFFT modifies C2R input buffer for some non-strided
FFT plans.
* There is a known issue with certain cuFFT plans that
causes an assertion in the execution phase of certain
plans. This applies to plans with all of the following
characteristics: real input to complex output (R2C),
in-place, native compatibility mode, certain even
transform sizes, and more than one batch.
2.3. cuRAND Library
2.3.1. cuRAND: Release 11.3
* Resolved Issues
* Fixed inconsistency between random numbers generated
by GPU and host generators when
CURAND_ORDERING_PSEUDO_LEGACY ordering is selected for
certain generator types.
2.3.2. cuRAND: Release 11.0 Update 1
* Resolved Issues
* Fixed an issue that caused linker errors about the
multiple definitions of mtgp32dc_params_fast_11213 and
mtgpdc_params_11213_num when including
curand_mtgp32dc_p_11213.h in different compilation
units.
2.3.3. cuRAND: Release 11.0
* Resolved Issues
* Fixed an issue that caused linker errors about the
multiple definitions of mtgp32dc_params_fast_11213 and
mtgpdc_params_11213_num when including
curand_mtgp32dc_p_11213.h in different compilation
units.
2.3.4. cuRAND: Release 11.0 RC
* Resolved Issues
* Introduced CURAND_ORDERING_PSEUDO_LEGACY ordering.
Starting with CUDA 10.0, the ordering of random
numbers returned by MTGP32 and MRG32k3a generators are
no longer the same as previous releases despite being
guaranteed by the documentation for the
CURAND_ORDERING_PSEUDO_DEFAULT setting. The
CURAND_ORDERING_PSEUDO_LEGACY provides pre-CUDA 10.0
ordering for MTGP32 and MRG32k3a generators.
* Starting with CUDA 11.0 CURAND_ORDERING_PSEUDO_DEFAULT
is the same as CURAND_ORDERING_PSEUDO_BEST for all
generators except MT19937. Only
CURAND_ORDERING_PSEUDO_LEGACY is guaranteed to provide
the same for all future cuRAND releases.
2.4. cuSOLVER Library
2.4.1. cuSOLVER: Release 11.4
* New Features
* Introducing cusolverDnXtrtri, a new generic API for
triangular matrix inversion (trtri).
* Introducing cusolverDnXsytrs, a new generic API for
solving systems of linear equations using a given
factorized symmetric matrix from SYTRF.
2.4.2. cuSOLVER: Release 11.3
* Known Issues
* For values N<=16, cusolverDn[S|D|C|Z]syevjBatched hits
out-of-bound access and may deliver the wrong result.
The workaround is to pad the matrix A with a diagonal
matrix D such that the dimension of [A 0 ; 0 D] is
bigger than 16. The diagonal entry D(j,j) must be
bigger than maximum eigenvalue of A, for example,
norm(A, ‘fro’). After the syevj, W(0:n-1) contains
the eigenvalues and A(0:n-1,0:n-1) contains the
eigenvectors.
2.4.3. cuSOLVER: Release 11.2 Update 2
* New Features
* New singular value decomposition (GESVDR) is added.
GESVDR computes partial spectrum with random sampling,
an order of magnitude faster than GESVD.
* libcusolver.so no longer links libcublas_static.a;
instead, it depends on libcublas.so. This reduces the
binary size of libcusolver.so. However, it breaks
backward compatibility. The user has to link
libcusolver.so with the correct version of
libcublas.so.
2.4.4. cuSOLVER: Release 11.2
* Resolved Issues
* cusolverDnIRSXgels sometimes returned
CUSOLVER_STATUS_INTERNAL_ERROR when the precision is
‘z’. This issue has been fixed in CUDA 11.2; now
cusolverDnIRSXgels works for all precisions.
* ZSYTRF sometimes returned
CUSOLVER_STATUS_INTERNAL_ERROR due to insufficient
resources to launch the kernel. This issue has been
fixed in CUDA 11.2.
* GETRF returned early without finishing the whole
factorization when the matrix was singular. This issue
has been fixed in CUDA 11.2.
2.4.5. cuSOLVER: Release 11.1 Update 1
* Resolved Issues
* cusolverDnDDgels reports IRS_NOT_SUPPORTED when m > n.
The issue has been fixed in release 11.1 U1, so
cusolverDnDDgels will support m > n.
* cusolverMgDeviceSelect can consume over 1GB device
memory. The issue has been fixed in release 11.1 U1.
The hidden memory allocation inside cusolverMG handle
is about 30 MB per device.
* Known Issues
*
cusolverDnIRSXgels may return
CUSOLVER_STATUS_INTERNAL_ERROR. when the precision is
‘z’ due to insufficient workspace which causes
illegal memory access.
The cusolverDnIRSXgels_bufferSize() does not report
the correct size of workspace. To workaround the
issue, the user has to add more workspace than what is
reported by cusolverDnIRSXgels_bufferSize(). For
example, if x is the size of workspace returned by
cusolverDnIRSXgels_bufferSize(), then the user has to
allocate (x + min(m,n)*sizeof(cuDoubleComplex)) bytes.
2.4.6. cuSOLVER: Release 11.1
* New Features
* Added new 64-bit APIs:
* cusolverDnXpotrf_bufferSize
* cusolverDnXpotrf
* cusolverDnXpotrs
* cusolverDnXgeqrf_bufferSize
* cusolverDnXgeqrf
* cusolverDnXgetrf_bufferSize
* cusolverDnXgetrf
* cusolverDnXgetrs
* cusolverDnXsyevd_bufferSize
* cusolverDnXsyevd
* cusolverDnXsyevdx_bufferSize
* cusolverDnXsyevdx
* cusolverDnXgesvd_bufferSize
* cusolverDnXgesvd
* Added a new SVD algorithm based on polar
decomposition, called GESVDP which uses the new 64-bit
API, including cusolverDnXgesvdp_bufferSize and
cusolverDnXgesvdp.
* Deprecated FeaturesThe following 64-bit APIs are
deprecated:
* cusolverDnPotrf_bufferSize
* cusolverDnPotrf
* cusolverDnPotrs
* cusolverDnGeqrf_bufferSize
* cusolverDnGeqrf
* cusolverDnGetrf_bufferSize
* cusolverDnGetrf
* cusolverDnGetrs
* cusolverDnSyevd_bufferSize
* cusolverDnSyevd
* cusolverDnSyevdx_bufferSize
* cusolverDnSyevdx
* cusolverDnGesvd_bufferSize
* cusolverDnGesvd
2.4.7. cuSOLVER: Release 11.0
* New Features
* Add 64-bit API of GESVD. The new routine
cusolverDnGesvd_bufferSize() fills the missing
parameters in 32-bit API
cusolverDn[S|D|C|Z]gesvd_bufferSize() such that it can
estimate the size of the workspace accurately.
* Added the single process multi-GPU Cholesky
factorization capabilities POTRF, POTRS and POTRI in
cusolverMG library.
* Resolved Issues
* Fixed an issue where SYEVD/SYGVD would fail and return
error code 7 if the matrix is zero and the dimension
is bigger than 25.
2.5. cuSPARSE Library
2.5.1. cuSPARSE: Release 11.4 Update 1
* Resolved Issues
* cusparseSpSV and cusparseSpSM could produce wrong
results
* cusparseSpSV and cusparseSpSM do not work correctly
when vecX == vecY or matB == matC.
2.5.2. cuSPARSE: Release 11.4
* Known Issues
* cusparseSpSV and cusparseSpSM could produce wrong
results
* cusparseSpSV and cusparseSpSM do not work correctly
when vecX == vecY or matB == matC.
2.5.3. cuSPARSE: Release 11.3 Update 1
* New Features
* Introduced a new routine for sparse matrix - sparse
matrix multiplication (cusparseSpGEMMreuse) where the
output matrix structure is reused for multiple
computation. The new routine supports CSR storage
format and mixed-precision computation.
* Sparse triangular solver adds support for COO format.
* Introduced a new routine for sparse triangular solver
with multiple right-hand sides cusparseSpSM().
* cusparseDenseToSparse() routine adds the conversion
from dense matrix (row-major/column-major) to
Blocked-ELL format.
* Blocke-ELL format now support empty blocks
* Better performance for Blocked-ELL SpMM with block
size > 64, double data type, and alignments smaller
than 128-byte on NVIDIA Ampere sm80.
* All cuSPARSE APIs are now asynchronous on platforms
that support stream ordered memory allocators
https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#stream-ordered-querying-memory-support.
* Improved NTVX trace with distinction between light
calls and kernel routines
* Resolved Issues
* cusparseCnnz_compress produced wrong results when the
number of rows are greater than 128 * resident CTAs.
* cusparseSnnz produced wrong results for some
particular sparsity pattern.
* Deprecated Features
* cusparseXcsrsm2_zeroPivot, cusparseXcsrsm2_solve,
cusparseXcsrsm2_analysis, and
cusparseScsrsm2_bufferSizeExt have been deprecated in
favor of cusparseSpSM Generic APIs
2.5.4. cuSPARSE: Release 11.3
* New FeaturesAdded new routine cusparesSpSV for sparse
triangular solver with better performance. The new Generic
API supports:
* CSR storage format
* Non-transpose, transpose, and transpose-conjugate
operations
* Upper, lower fill mode
* Unit, non-unit diagonal type
* 32-bit and 64-bit indices
* Uniform data type computation
* Deprecated Features
* cusparseScsrsv2_analysis, cusparseScsrsv2_solve,
cusparseXcsrsv2_zeroPivot, and
cusparseScsrsv2_bufferSize have been deprecated in
favor of cusparseSpSV.
2.5.5. cuSPARSE: Release 11.2 Update 2
* Resolved Issues
* cusparseDestroy(NULL) no longer crashes on Windows.
* Known Issues
* cusparseDestroySpVec, cusparseDestroyDnVec,
cusparseDestroySpMat, cusparseDestroyDnMat,
cusparseDestroy with NULL argument could cause
segmentation fault on Windows.
2.5.6. cuSPARSE: Release 11.2 Update 1
* New Features
* New Tensor Core-accelerated Block Sparse Matrix -
Matrix Multiplication (cusparseSpMM) and introduction
of the Blocked-Ellpack storage format.
* New algorithms for CSR/COO Sparse Matrix - Vector
Multiplication (cusparseSpMV) with better performance.
* Extended functionalities for cusparseSpMV:
* Support for the CSC format.
* Support for regular/complex bfloat16 data types
for both uniform and mixed-precision computation.
* Support for mixed regular-complex data type
computation.
* Support for deterministic and non-deterministic
computation.
* New algorithm (CUSPARSE_SPMM_CSR_ALG3) for Sparse
Matrix - Matrix Multiplication (cusparseSpMM) with
better performance especially for small matrices.
* New routine for Sampled Dense Matrix - Dense Matrix
Multiplication (cusparseSDDMM) which deprecated
cusparseConstrainedGeMM and provides better
performance.
* Better accuracy of cusparseAxpby, cusparseRot,
cusparseSpVV for bfloat16 and half regular/complex
data types.
* All routines support NVTX annotation for enhancing the
profiler time line on complex applications.
* Resolved Issues
* cusparseAxpby, cusparseGather, cusparseScatter,
cusparseRot, cusparseSpVV, cusparseSpMV now support
zero-size matrices.
* cusparseCsr2cscEx2 now correctly handles empty
matrices (nnz = 0).
* cusparseXcsr2csr_compress now uses 2-norm for the
comparison of complex values instead of only the real
part.
* Known Issues
cusparseDestroySpVec, cusparseDestroyDnVec,
cusparseDestroySpMat, cusparseDestroyDnMat,
cusparseDestroy with NULL argument could cause
segmentation fault on Windows.
* Deprecated Features
* cusparseConstrainedGeMM has been deprecated in favor
of cusparseSDDMM.
* cusparseCsrmvEx has been deprecated in favor of
cusparseSpMV.
* COO Array of Structure (CooAoS) format has been
deprecated including cusparseCreateCooAoS,
cusparseCooAoSGet, and its support for cusparseSpMV.
2.5.7. cuSPARSE: Release 11.2
* Known Issues
* cusparseXdense2csr provides incorrect results for some
matrix sizes.
2.5.8. cuSPARSE: Release 11.1 Update 1
* New Features
* cusparseSparseToDense
* CSR, CSC, or COO conversion to dense
representation
* Support row-major and column-major layouts
* Support all data types
* Support 32-bit and 64-bit indices
* Provide performance 3x higher than
cusparseXcsc2dense, cusparseXcsr2dense
* cusparseDenseToSparse
* Dense representation to CSR, CSC, or COO
* Support row-major and column-major layouts
* Support all data types
* Support 32-bit and 64-bit indices
* Provide performance 3x higher than
cusparseXcsc2dense, cusparseXcsr2dense
* Known Issues
* cusparseXdense2csr provides incorrect results for some
matrix sizes.
* Deprecated Features
* Legacy conversion routines: cusparseXcsc2dense,
cusparseXcsr2dense, cusparseXdense2csc,
cusparseXdense2csr
2.5.9. cuSPARSE: Release 11.0
* New Features
* Added new Generic APIs for Axpby (cusparseAxpby),
Scatter (cusparseScatter), Gather (cusparseGather),
Givens rotation (cusparseRot). __nv_bfloat16/
__nv_bfloat162 data types and 64-bit indices are also
supported.
*
This release adds the following features for
cusparseSpMM:
* Support for row-major layout for cusparseSpMM for
both CSR and COO format
* Support for 64-bit indices
* Support for __nv_bfloat16 and __nv_bfloat162 data
types
* Support for the following strided batch mode:
*
Ci=A⋅Bi
*
Ci=Ai⋅B
*
Ci=Ai⋅Bi
2.5.10. cuSPARSE: Release 11.0 RC
* New Features
* Added new Generic APIs for Axpby (cusparseAxpby),
Scatter (cusparseScatter), Gather (cusparseGather),
Givens rotation (cusparseRot). __nv_bfloat16/
__nv_bfloat162 data types and 64-bit indices are also
supported.
*
This release adds the following features for
cusparseSpMM:
* Support for row-major layout for cusparseSpMM for
both CSR and COO format
* Support for 64-bit indices
* Support for __nv_bfloat16 and __nv_bfloat162 data
types
* Support for the following strided batch mode:
*
Ci=A⋅Bi
*
Ci=Ai⋅B
*
Ci=Ai⋅Bi
* Added new generic APIs and improved performance for
sparse matrix-sparse matrix multiplication (SpGEMM):
cusparseSpGEMM_workEstimation, cusparseSpGEMM_compute,
and cusparseSpGEMM_copy.
* SpVV: added support for __nv_bfloat16.
* Deprecated FeaturesThe following functions have been
removed:
* cusparse<t>gemmi()
* cusparseXaxpyi, cusparseXgthr, cusparseXgthrz,
cusparseXroti, cusparseXsctr
* Hybrid format enums and helper functions:
cusparseHybPartition_t, cusparseHybPartition_t,
cusparseCreateHybMat, cusparseDestroyHybMat
* Triangular solver enums and helper functions:
cusparseSolveAnalysisInfo_t,
cusparseCreateSolveAnalysisInfo,
cusparseDestroySolveAnalysisInfo
* Sparse dot product: cusparseXdoti, cusparseXdotci
* Sparse matrix-vector multiplication: cusparseXcsrmv,
cusparseXcsrmv_mp
* Sparse matrix-matrix multiplication: cusparseXcsrmm,
cusparseXcsrmm2
* Sparse triangular-single vector solver:
cusparseXcsrsv_analysis, cusparseCsrsv_analysisEx,
cusparseXcsrsv_solve, cusparseCsrsv_solveEx
* Sparse triangular-multiple vectors solver:
cusparseXcsrsm_analysis, cusparseXcsrsm_solve
* Sparse hybrid format solver: cusparseXhybsv_analysis,
cusparseShybsv_solve
* Extra functions: cusparseXcsrgeamNnz, cusparseScsrgeam,
cusparseXcsrgemmNnz, cusparseXcsrgemm
* Incomplete Cholesky Factorization, level 0:
cusparseXcsric0
* Incomplete LU Factorization, level 0: cusparseXcsrilu0,
cusparseCsrilu0Ex
* Tridiagonal Solver: cusparseXgtsv,
cusparseXgtsv_nopivot
* Batched Tridiagonal Solver: cusparseXgtsvStridedBatch
* Reordering: cusparseXcsc2hyb, cusparseXcsr2hyb,
cusparseXdense2hyb, cusparseXhyb2csc, cusparseXhyb2csr,
cusparseXhyb2dense
The following functions have been deprecated:
* SpGEMM: cusparseXcsrgemm2_bufferSizeExt,
cusparseXcsrgemm2Nnz, cusparseXcsrgemm2
2.6. Math Library
2.6.1. CUDA Math: Release 11.5
* Deprecations
* The following undocumented CUDA Math APIs are
deprecated and will be removed in a future release.
Please consider switching to similar intrinsic APIs
documented here:
https://docs.nvidia.com/cuda/cuda-math-api/index.html
* __device__ int mulhi(const int a, const int b)
* __device__ unsigned int mulhi(const unsigned int
a, const unsigned int b)
* __device__ unsigned int mulhi(const int a, const
unsigned int b)
* __device__ unsigned int mulhi(const unsigned int
a, const int b)
* __device__ long long int mul64hi(const long long
int a, const long long int b)
* __device__ unsigned long long int mul64hi(const
unsigned long long int a, const unsigned long long
int b)
* __device__ unsigned long long int mul64hi(const
long long int a, const unsigned long long int b)
* __device__ unsigned long long int mul64hi(const
unsigned long long int a, const long long int b)
* __device__ int float_as_int(const float a)
* __device__ float int_as_float(const int a)
* __device__ unsigned int float_as_uint(const float
a)
* __device__ float uint_as_float(const unsigned int
a)
* __device__ float saturate(const float a)
* __device__ int mul24(const int a, const int b)
* __device__ unsigned int umul24(const unsigned int
a, const unsigned int b)
* __device__ int float2int(const float a, const enum
cudaRoundMode mode = cudaRoundZero)
* __device__ unsigned int float2uint(const float a,
const enum cudaRoundMode mode = cudaRoundZero)
* __device__ float int2float(const int a, const enum
cudaRoundMode mode = cudaRoundNearest)
* __device__ float uint2float(const unsigned int a,
const enum cudaRoundMode mode = cudaRoundNearest)
2.6.2. CUDA Math: Release 11.4
Beginning in 2022, the NVIDIA Math Libraries official hardware
support will follow an N-2 policy, where N is an x100 series
GPU.
2.6.3. CUDA Math: Release 11.3
* Resolved Issues
* Previous releases of CUDA were potentially delivering
incorrect results in some Linux distributions for the
following host Math APIs: sinpi, cospi, sincospi,
sinpif, cospif, sincospif. If passed huge inputs like
7.3748776e+15 or 8258177.5 the results were not equal
to 0 or 1. These have been corrected with this
release.
2.6.4. CUDA Math: Release 11.1
* New Features
* Added host support for half and nv_bfloat16 converts
to/from integer types.
* Added __hcmadd() device only API for fast half2 and
nv_bfloat162 based complex multiply-accumulate.
2.6.5. CUDA Math: Release 11.0 Update 1
* Resolved Issues
* nv_bfloat16 comparison functions could trigger a fault
with misaligned addresses.
* Performance improvements in half and nv_bfloat16 basic
arithmetic implementations.
2.6.6. CUDA Math: Release 11.0 RC
* New Features
* Add arithmetic support for __nv_bfloat16
floating-point data type with 8 bits of exponent, 7
explicit bits of mantissa.
* Performance and accuracy improvements in single
precision math functions: fmodf, expf, exp10f, sinhf,
and coshf.
* Resolved Issues
*
Corrected documented maximum ulp error thresholds in
erfcinvf and powf.
* Improved cuda_fp16.h interoperability with Visual
Studio C++ compiler.
* Updated libdevice user guide and CUDA math API
definitions for j1, j1f, fmod, fmodf, ilogb, and
ilogbf math functions.
2.7. NVIDIA Performance Primitives (NPP)
2.7.1. NPP: Release 11.5
* New Features
* New APIs added to compute Signed Anti-aliased Distance
Transform using PBA, the anti-aliased Euclidean
distance between pixel sites in images. This will
improve the accuracy of distance transform.
* nppiSignedDistanceTransformAbsPBA_xxxxx_C1R_Ctx()
– Input and output combination supports (xxxxxx)
- 32f, 32f64f, 64f
* New API for Absolute Manhattan distance transform;
another method to improve the accuracy of distance
transform using Manhattan distance transform between
pixels.
* nppiDistanceTransformAbsPBA_xxxxx_C1R_Ctx() –
Input and output combination supports (xxxxxx) -
8u16u, 8s16u, 16u16u, 16s16u, 8u32f, 8s32f,
16u32f, 16s32f, 8u64f, 8s64f, 16u64f, 16s64f,
32f64f, 64f
* Resolved Issues
* Fix an issue in FilterMedian() API with add
interpolation when mask even size.
* Improved Contour function performance by parallelizing
more of it and also improving quality.
* Resolved an issue with Alpha composition used to
accumulate output buffers multiple times.
* Resolved an issue with nppiLabelMarkersUF_8u32u_C1R
column processing incorrect results.
2.7.2. NPP: Release 11.4
* New Features
* New API FindContours .FindContours can be explained
simply as a curve joining all the continuous points
(along the boundary), having the same color or
intensity. The contours are a useful tool for shape
analysis and object detection and recognition.
2.7.3. NPP: Release 11.3
* New Features
* Added nppiDistanceTransformPBA functions.
2.7.4. NPP: Release 11.2 Update 2
* New Features
* Added nppiDistanceTransformPBA functions.
2.7.5. NPP: Release 11.2 Update 1
* New FeaturesNew APIs added to compute Distance Transform
using Parallel Banding Algorithm (PBA):
* nppiDistanceTransformPBA_xxxxx_C1R_Ctx() – where
xxxxx specifies the input and output combination:
8u16u, 8s16u, 16u16u, 16s16u, 8u32f, 8s32f, 16u32f,
16s32f
* nppiSignedDistanceTransformPBA_32f_C1R_Ctx()
* Resolved Issues
* Fixed the issue in which Label Markers adds zero pixel
as object region.
2.7.6. NPP: Release 11.0
* New Features
* Batched Image Label Markers Compression that removes
sparseness between marker label IDs output from
LabelMarkers call.
* Image Flood Fill functionality fills a connected
region of an image with a specified new value.
* Stability and performance fixes to Image Label Markers
and Image Label Markers Compression.
2.7.7. NPP: Release 11.0 RC
* New Features
* Batched Image Label Markers Compression that removes
sparseness between marker label IDs output from
LabelMarkers call.
* Image Flood Fill functionality fills a connected
region of an image with a specified new value.
* Added batching support for nppiLabelMarkersUF
functions.
* Added the nppiCompressMarkerLabelsUF_32u_C1IR
function.
*
Added nppiSegmentWatershed functions.
* Added sample apps on GitHub demonstrating the use of
NPP application managed stream contexts along with
watershed segmentation and batched and compressed UF
image label markers functions.
* Added support for non-blocking streams.
* Resolved Issues
* Stability and performance fixes to Image Label Markers
and Image Label Markers Compression.
* Improved quality of nppiLabelMarkersUF functions.
* nppiCompressMarkerLabelsUF_32u_C1IR can now handle a
huge number of labels generated by the
nppiLabelMarkersUF function.
* Known Issues
* The nppiCopy API is limited by CUDA thread for large
image size. Maximum image limits is a minimum of 16 *
65,535 = 1,048,560 horizontal pixels of any data type
and number of channels and 8 * 65,535 = 524,280
vertical pixels for a maximum total of 549,739,036,800
pixels.
2.8. nvJPEG Library
2.8.1. nvJPEG: Release 11.4
* Resolved Issues
* Additional subsampling added to solve the
NVJPEG_CSS_2x4.
2.8.2. nvJPEG: Release 11.2 Update 1
* New FeaturesnvJPEG decoder added new APIs to support
region of interest (ROI) based decoding for batched
hardware decoder:
* nvjpegDecodeBatchedEx()
* nvjpegDecodeBatchedSupportedEx()
2.8.3. nvJPEG: Release 11.1 Update 1
* New Features
* Added error handling capabilities for nonstandard JPEG
images.
2.8.4. nvJPEG: Release 11.0 Update 1
* Known Issues
* NVJPEG_BACKEND_GPU_HYBRID has an issue when handling
bit-streams which have corruption in the scan.
2.8.5. nvJPEG: Release 11.0
* New Features
* nvJPEG allows the user to allocate separate memory
pools for each chroma subsampling format. This helps
avoid memory re-allocation overhead. This can be
controlled by passing the newly added flag
NVJPEG_FLAGS_ENABLE_MEMORY_POOLS to the nvjpegCreateEx
API.
* nvJPEG encoder now allow compressed bitstream on the
GPU Memory.
2.8.6. nvJPEG: Release 11.0 RC
* New Features
* nvJPEG allows the user to allocate separate memory
pools for each chroma subsampling format. This helps
avoid memory re-allocation overhead. This can be
controlled by passing the newly added flag
NVJPEG_FLAGS_ENABLE_MEMORY_POOLS to the nvjpegCreateEx
API.
* nvJPEG encoder now allow compressed bitstream on the
GPU Memory.
* Hardware accelerated decode is now supported on NVIDIA
A100.
* The nvJPEG decode API (nvjpegDecodeJpeg()) now has the
flexibility to select the backend when creating
nvjpegJpegDecoder_t object. The user has the option to
call this API instead of making three separate calls
to nvjpegDecodeJpegHost(),
nvjpegDecodeJpegTransferToDevice(), and
nvjpegDecodeJpegDevice().
* Known Issues
* NVJPEG_BACKEND_GPU_HYBRID has an issue when handling
bit-streams which have corruption in the scan.
* Deprecated Features
The following multiphase APIs have been removed:
*
nvjpegStatus_t NVJPEGAPI nvjpegDecodePhaseOne
*
nvjpegStatus_t NVJPEGAPI nvjpegDecodePhaseTwo
*
nvjpegStatus_t NVJPEGAPI nvjpegDecodePhaseThree
*
nvjpegStatus_t NVJPEGAPI nvjpegDecodeBatchedPhaseOne
*
nvjpegStatus_t NVJPEGAPI nvjpegDecodeBatchedPhaseTwo
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