add and update samples for CUDA 11.6

This commit is contained in:
Rutwik Choughule
2022-01-13 11:35:24 +05:30
parent 11de19f00c
commit 2e41896e1b
4615 changed files with 178150 additions and 181350 deletions

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{
"configurations": [
{
"name": "Linux",
"includePath": [
"${workspaceFolder}/**",
"${workspaceFolder}/../../../Common"
],
"defines": [],
"compilerPath": "/usr/local/cuda/bin/nvcc",
"cStandard": "gnu17",
"cppStandard": "gnu++14",
"intelliSenseMode": "linux-gcc-x64",
"configurationProvider": "ms-vscode.makefile-tools"
}
],
"version": 4
}

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{
"recommendations": [
"nvidia.nsight-vscode-edition",
"ms-vscode.cpptools",
"ms-vscode.makefile-tools"
]
}

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{
"configurations": [
{
"name": "CUDA C++: Launch",
"type": "cuda-gdb",
"request": "launch",
"program": "${workspaceFolder}/convolutionSeparable"
}
]
}

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{
"version": "2.0.0",
"tasks": [
{
"label": "sample",
"type": "shell",
"command": "make dbg=1",
"problemMatcher": ["$nvcc"],
"group": {
"kind": "build",
"isDefault": true
}
}
]
}

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################################################################################
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of NVIDIA CORPORATION nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
################################################################################
#
# Makefile project only supported on Mac OS X and Linux Platforms)
#
################################################################################
# Location of the CUDA Toolkit
CUDA_PATH ?= /usr/local/cuda
##############################
# start deprecated interface #
##############################
ifeq ($(x86_64),1)
$(info WARNING - x86_64 variable has been deprecated)
$(info WARNING - please use TARGET_ARCH=x86_64 instead)
TARGET_ARCH ?= x86_64
endif
ifeq ($(ARMv7),1)
$(info WARNING - ARMv7 variable has been deprecated)
$(info WARNING - please use TARGET_ARCH=armv7l instead)
TARGET_ARCH ?= armv7l
endif
ifeq ($(aarch64),1)
$(info WARNING - aarch64 variable has been deprecated)
$(info WARNING - please use TARGET_ARCH=aarch64 instead)
TARGET_ARCH ?= aarch64
endif
ifeq ($(ppc64le),1)
$(info WARNING - ppc64le variable has been deprecated)
$(info WARNING - please use TARGET_ARCH=ppc64le instead)
TARGET_ARCH ?= ppc64le
endif
ifneq ($(GCC),)
$(info WARNING - GCC variable has been deprecated)
$(info WARNING - please use HOST_COMPILER=$(GCC) instead)
HOST_COMPILER ?= $(GCC)
endif
ifneq ($(abi),)
$(error ERROR - abi variable has been removed)
endif
############################
# end deprecated interface #
############################
# architecture
HOST_ARCH := $(shell uname -m)
TARGET_ARCH ?= $(HOST_ARCH)
ifneq (,$(filter $(TARGET_ARCH),x86_64 aarch64 sbsa ppc64le armv7l))
ifneq ($(TARGET_ARCH),$(HOST_ARCH))
ifneq (,$(filter $(TARGET_ARCH),x86_64 aarch64 sbsa ppc64le))
TARGET_SIZE := 64
else ifneq (,$(filter $(TARGET_ARCH),armv7l))
TARGET_SIZE := 32
endif
else
TARGET_SIZE := $(shell getconf LONG_BIT)
endif
else
$(error ERROR - unsupported value $(TARGET_ARCH) for TARGET_ARCH!)
endif
# sbsa and aarch64 systems look similar. Need to differentiate them at host level for now.
ifeq ($(HOST_ARCH),aarch64)
ifeq ($(CUDA_PATH)/targets/sbsa-linux,$(shell ls -1d $(CUDA_PATH)/targets/sbsa-linux 2>/dev/null))
HOST_ARCH := sbsa
TARGET_ARCH := sbsa
endif
endif
ifneq ($(TARGET_ARCH),$(HOST_ARCH))
ifeq (,$(filter $(HOST_ARCH)-$(TARGET_ARCH),aarch64-armv7l x86_64-armv7l x86_64-aarch64 x86_64-sbsa x86_64-ppc64le))
$(error ERROR - cross compiling from $(HOST_ARCH) to $(TARGET_ARCH) is not supported!)
endif
endif
# When on native aarch64 system with userspace of 32-bit, change TARGET_ARCH to armv7l
ifeq ($(HOST_ARCH)-$(TARGET_ARCH)-$(TARGET_SIZE),aarch64-aarch64-32)
TARGET_ARCH = armv7l
endif
# operating system
HOST_OS := $(shell uname -s 2>/dev/null | tr "[:upper:]" "[:lower:]")
TARGET_OS ?= $(HOST_OS)
ifeq (,$(filter $(TARGET_OS),linux darwin qnx android))
$(error ERROR - unsupported value $(TARGET_OS) for TARGET_OS!)
endif
# host compiler
ifeq ($(TARGET_OS),darwin)
ifeq ($(shell expr `xcodebuild -version | grep -i xcode | awk '{print $$2}' | cut -d'.' -f1` \>= 5),1)
HOST_COMPILER ?= clang++
endif
else ifneq ($(TARGET_ARCH),$(HOST_ARCH))
ifeq ($(HOST_ARCH)-$(TARGET_ARCH),x86_64-armv7l)
ifeq ($(TARGET_OS),linux)
HOST_COMPILER ?= arm-linux-gnueabihf-g++
else ifeq ($(TARGET_OS),qnx)
ifeq ($(QNX_HOST),)
$(error ERROR - QNX_HOST must be passed to the QNX host toolchain)
endif
ifeq ($(QNX_TARGET),)
$(error ERROR - QNX_TARGET must be passed to the QNX target toolchain)
endif
export QNX_HOST
export QNX_TARGET
HOST_COMPILER ?= $(QNX_HOST)/usr/bin/arm-unknown-nto-qnx6.6.0eabi-g++
else ifeq ($(TARGET_OS),android)
HOST_COMPILER ?= arm-linux-androideabi-g++
endif
else ifeq ($(TARGET_ARCH),aarch64)
ifeq ($(TARGET_OS), linux)
HOST_COMPILER ?= aarch64-linux-gnu-g++
else ifeq ($(TARGET_OS),qnx)
ifeq ($(QNX_HOST),)
$(error ERROR - QNX_HOST must be passed to the QNX host toolchain)
endif
ifeq ($(QNX_TARGET),)
$(error ERROR - QNX_TARGET must be passed to the QNX target toolchain)
endif
export QNX_HOST
export QNX_TARGET
HOST_COMPILER ?= $(QNX_HOST)/usr/bin/q++
else ifeq ($(TARGET_OS), android)
HOST_COMPILER ?= aarch64-linux-android-clang++
endif
else ifeq ($(TARGET_ARCH),sbsa)
HOST_COMPILER ?= aarch64-linux-gnu-g++
else ifeq ($(TARGET_ARCH),ppc64le)
HOST_COMPILER ?= powerpc64le-linux-gnu-g++
endif
endif
HOST_COMPILER ?= g++
NVCC := $(CUDA_PATH)/bin/nvcc -ccbin $(HOST_COMPILER)
# internal flags
NVCCFLAGS := -m${TARGET_SIZE}
CCFLAGS :=
LDFLAGS :=
# build flags
ifeq ($(TARGET_OS),darwin)
LDFLAGS += -rpath $(CUDA_PATH)/lib
CCFLAGS += -arch $(HOST_ARCH)
else ifeq ($(HOST_ARCH)-$(TARGET_ARCH)-$(TARGET_OS),x86_64-armv7l-linux)
LDFLAGS += --dynamic-linker=/lib/ld-linux-armhf.so.3
CCFLAGS += -mfloat-abi=hard
else ifeq ($(TARGET_OS),android)
LDFLAGS += -pie
CCFLAGS += -fpie -fpic -fexceptions
endif
ifneq ($(TARGET_ARCH),$(HOST_ARCH))
ifeq ($(TARGET_ARCH)-$(TARGET_OS),armv7l-linux)
ifneq ($(TARGET_FS),)
GCCVERSIONLTEQ46 := $(shell expr `$(HOST_COMPILER) -dumpversion` \<= 4.6)
ifeq ($(GCCVERSIONLTEQ46),1)
CCFLAGS += --sysroot=$(TARGET_FS)
endif
LDFLAGS += --sysroot=$(TARGET_FS)
LDFLAGS += -rpath-link=$(TARGET_FS)/lib
LDFLAGS += -rpath-link=$(TARGET_FS)/usr/lib
LDFLAGS += -rpath-link=$(TARGET_FS)/usr/lib/arm-linux-gnueabihf
endif
endif
ifeq ($(TARGET_ARCH)-$(TARGET_OS),aarch64-linux)
ifneq ($(TARGET_FS),)
GCCVERSIONLTEQ46 := $(shell expr `$(HOST_COMPILER) -dumpversion` \<= 4.6)
ifeq ($(GCCVERSIONLTEQ46),1)
CCFLAGS += --sysroot=$(TARGET_FS)
endif
LDFLAGS += --sysroot=$(TARGET_FS)
LDFLAGS += -rpath-link=$(TARGET_FS)/lib -L$(TARGET_FS)/lib
LDFLAGS += -rpath-link=$(TARGET_FS)/lib/aarch64-linux-gnu -L$(TARGET_FS)/lib/aarch64-linux-gnu
LDFLAGS += -rpath-link=$(TARGET_FS)/usr/lib -L$(TARGET_FS)/usr/lib
LDFLAGS += -rpath-link=$(TARGET_FS)/usr/lib/aarch64-linux-gnu -L$(TARGET_FS)/usr/lib/aarch64-linux-gnu
LDFLAGS += --unresolved-symbols=ignore-in-shared-libs
CCFLAGS += -isystem=$(TARGET_FS)/usr/include -I$(TARGET_FS)/usr/include -I$(TARGET_FS)/usr/include/libdrm
CCFLAGS += -isystem=$(TARGET_FS)/usr/include/aarch64-linux-gnu -I$(TARGET_FS)/usr/include/aarch64-linux-gnu
endif
endif
ifeq ($(TARGET_ARCH)-$(TARGET_OS),aarch64-qnx)
NVCCFLAGS += -D_QNX_SOURCE
NVCCFLAGS += --qpp-config 8.3.0,gcc_ntoaarch64le
CCFLAGS += -DWIN_INTERFACE_CUSTOM -I/usr/include/aarch64-qnx-gnu
LDFLAGS += -lsocket
LDFLAGS += -L/usr/lib/aarch64-qnx-gnu
CCFLAGS += "-Wl\,-rpath-link\,/usr/lib/aarch64-qnx-gnu"
ifdef TARGET_OVERRIDE
LDFLAGS += -lslog2
endif
ifneq ($(TARGET_FS),)
LDFLAGS += -L$(TARGET_FS)/usr/lib
CCFLAGS += "-Wl\,-rpath-link\,$(TARGET_FS)/usr/lib"
LDFLAGS += -L$(TARGET_FS)/usr/libnvidia
CCFLAGS += "-Wl\,-rpath-link\,$(TARGET_FS)/usr/libnvidia"
CCFLAGS += -I$(TARGET_FS)/../include
endif
endif
endif
ifdef TARGET_OVERRIDE # cuda toolkit targets override
NVCCFLAGS += -target-dir $(TARGET_OVERRIDE)
endif
# Install directory of different arch
CUDA_INSTALL_TARGET_DIR :=
ifeq ($(TARGET_ARCH)-$(TARGET_OS),armv7l-linux)
CUDA_INSTALL_TARGET_DIR = targets/armv7-linux-gnueabihf/
else ifeq ($(TARGET_ARCH)-$(TARGET_OS),aarch64-linux)
CUDA_INSTALL_TARGET_DIR = targets/aarch64-linux/
else ifeq ($(TARGET_ARCH)-$(TARGET_OS),sbsa-linux)
CUDA_INSTALL_TARGET_DIR = targets/sbsa-linux/
else ifeq ($(TARGET_ARCH)-$(TARGET_OS),armv7l-android)
CUDA_INSTALL_TARGET_DIR = targets/armv7-linux-androideabi/
else ifeq ($(TARGET_ARCH)-$(TARGET_OS),aarch64-android)
CUDA_INSTALL_TARGET_DIR = targets/aarch64-linux-androideabi/
else ifeq ($(TARGET_ARCH)-$(TARGET_OS),armv7l-qnx)
CUDA_INSTALL_TARGET_DIR = targets/ARMv7-linux-QNX/
else ifeq ($(TARGET_ARCH)-$(TARGET_OS),aarch64-qnx)
CUDA_INSTALL_TARGET_DIR = targets/aarch64-qnx/
else ifeq ($(TARGET_ARCH),ppc64le)
CUDA_INSTALL_TARGET_DIR = targets/ppc64le-linux/
endif
# Debug build flags
ifeq ($(dbg),1)
NVCCFLAGS += -g -G
BUILD_TYPE := debug
else
BUILD_TYPE := release
endif
ALL_CCFLAGS :=
ALL_CCFLAGS += $(NVCCFLAGS)
ALL_CCFLAGS += $(EXTRA_NVCCFLAGS)
ALL_CCFLAGS += $(addprefix -Xcompiler ,$(CCFLAGS))
ALL_CCFLAGS += $(addprefix -Xcompiler ,$(EXTRA_CCFLAGS))
SAMPLE_ENABLED := 1
ALL_LDFLAGS :=
ALL_LDFLAGS += $(ALL_CCFLAGS)
ALL_LDFLAGS += $(addprefix -Xlinker ,$(LDFLAGS))
ALL_LDFLAGS += $(addprefix -Xlinker ,$(EXTRA_LDFLAGS))
# Common includes and paths for CUDA
INCLUDES := -I../../../Common
LIBRARIES :=
################################################################################
# Gencode arguments
ifeq ($(TARGET_ARCH),$(filter $(TARGET_ARCH),armv7l aarch64 sbsa))
SMS ?= 53 61 70 72 75 80 86 87
else
SMS ?= 35 37 50 52 60 61 70 75 80 86
endif
ifeq ($(SMS),)
$(info >>> WARNING - no SM architectures have been specified - waiving sample <<<)
SAMPLE_ENABLED := 0
endif
ifeq ($(GENCODE_FLAGS),)
# Generate SASS code for each SM architecture listed in $(SMS)
$(foreach sm,$(SMS),$(eval GENCODE_FLAGS += -gencode arch=compute_$(sm),code=sm_$(sm)))
# Generate PTX code from the highest SM architecture in $(SMS) to guarantee forward-compatibility
HIGHEST_SM := $(lastword $(sort $(SMS)))
ifneq ($(HIGHEST_SM),)
GENCODE_FLAGS += -gencode arch=compute_$(HIGHEST_SM),code=compute_$(HIGHEST_SM)
endif
endif
ALL_CCFLAGS += --threads 0 --std=c++11
ifeq ($(SAMPLE_ENABLED),0)
EXEC ?= @echo "[@]"
endif
################################################################################
# Target rules
all: build
build: convolutionSeparable
check.deps:
ifeq ($(SAMPLE_ENABLED),0)
@echo "Sample will be waived due to the above missing dependencies"
else
@echo "Sample is ready - all dependencies have been met"
endif
convolutionSeparable.o:convolutionSeparable.cu
$(EXEC) $(NVCC) $(INCLUDES) $(ALL_CCFLAGS) $(GENCODE_FLAGS) -o $@ -c $<
convolutionSeparable_gold.o:convolutionSeparable_gold.cpp
$(EXEC) $(NVCC) $(INCLUDES) $(ALL_CCFLAGS) $(GENCODE_FLAGS) -o $@ -c $<
main.o:main.cpp
$(EXEC) $(NVCC) $(INCLUDES) $(ALL_CCFLAGS) $(GENCODE_FLAGS) -o $@ -c $<
convolutionSeparable: convolutionSeparable.o convolutionSeparable_gold.o main.o
$(EXEC) $(NVCC) $(ALL_LDFLAGS) $(GENCODE_FLAGS) -o $@ $+ $(LIBRARIES)
$(EXEC) mkdir -p ../../../bin/$(TARGET_ARCH)/$(TARGET_OS)/$(BUILD_TYPE)
$(EXEC) cp $@ ../../../bin/$(TARGET_ARCH)/$(TARGET_OS)/$(BUILD_TYPE)
run: build
$(EXEC) ./convolutionSeparable
testrun: build
clean:
rm -f convolutionSeparable convolutionSeparable.o convolutionSeparable_gold.o main.o
rm -rf ../../../bin/$(TARGET_ARCH)/$(TARGET_OS)/$(BUILD_TYPE)/convolutionSeparable
clobber: clean

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<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE entry SYSTEM "SamplesInfo.dtd">
<entry>
<name>convolutionSeparable</name>
<cuda_api_list>
<toolkit>cudaMemcpyToSymbol</toolkit>
<toolkit>cudaFree</toolkit>
<toolkit>cudaDeviceSynchronize</toolkit>
<toolkit>cudaMalloc</toolkit>
<toolkit>cudaMemcpy</toolkit>
</cuda_api_list>
<description><![CDATA[This sample implements a separable convolution filter of a 2D signal with a gaussian kernel.]]></description>
<devicecompilation>whole</devicecompilation>
<includepaths>
<path>./</path>
<path>../</path>
<path>../../../Common</path>
</includepaths>
<keyconcepts>
<concept level="basic">Image Processing</concept>
<concept level="basic">Data Parallel Algorithms</concept>
</keyconcepts>
<keywords>
<keyword>CUDA</keyword>
<keyword>DSP</keyword>
<keyword>separable convolution</keyword>
<keyword>finite impulse-response filter</keyword>
<keyword>Image Processing</keyword>
</keywords>
<libraries>
</libraries>
<librarypaths>
</librarypaths>
<nsight_eclipse>true</nsight_eclipse>
<primary_file>main.cpp</primary_file>
<scopes>
<scope>1:CUDA Basic Topics</scope>
<scope>1:Data-Parallel Algorithms</scope>
<scope>3:Linear Algebra</scope>
<scope>2:Image Processing</scope>
<scope>2:Computer Vision</scope>
</scopes>
<sm-arch>sm35</sm-arch>
<sm-arch>sm37</sm-arch>
<sm-arch>sm50</sm-arch>
<sm-arch>sm52</sm-arch>
<sm-arch>sm53</sm-arch>
<sm-arch>sm60</sm-arch>
<sm-arch>sm61</sm-arch>
<sm-arch>sm70</sm-arch>
<sm-arch>sm72</sm-arch>
<sm-arch>sm75</sm-arch>
<sm-arch>sm80</sm-arch>
<sm-arch>sm86</sm-arch>
<sm-arch>sm87</sm-arch>
<supported_envs>
<env>
<arch>x86_64</arch>
<platform>linux</platform>
</env>
<env>
<platform>windows7</platform>
</env>
<env>
<arch>x86_64</arch>
<platform>macosx</platform>
</env>
<env>
<arch>arm</arch>
</env>
<env>
<arch>sbsa</arch>
</env>
<env>
<arch>ppc64le</arch>
<platform>linux</platform>
</env>
</supported_envs>
<supported_sm_architectures>
<include>all</include>
</supported_sm_architectures>
<title>CUDA Separable Convolution</title>
<type>exe</type>
<whitepaper>doc\convolutionSeparable.pdf</whitepaper>
</entry>

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# convolutionSeparable - CUDA Separable Convolution
## Description
This sample implements a separable convolution filter of a 2D signal with a gaussian kernel.
## Key Concepts
Image Processing, Data Parallel Algorithms
## Supported SM Architectures
[SM 3.5 ](https://developer.nvidia.com/cuda-gpus) [SM 3.7 ](https://developer.nvidia.com/cuda-gpus) [SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus)
## Supported OSes
Linux, Windows
## Supported CPU Architecture
x86_64, ppc64le, armv7l
## CUDA APIs involved
### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html)
cudaMemcpyToSymbol, cudaFree, cudaDeviceSynchronize, cudaMalloc, cudaMemcpy
## Prerequisites
Download and install the [CUDA Toolkit 11.6](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
## Build and Run
### Windows
The Windows samples are built using the Visual Studio IDE. Solution files (.sln) are provided for each supported version of Visual Studio, using the format:
```
*_vs<version>.sln - for Visual Studio <version>
```
Each individual sample has its own set of solution files in its directory:
To build/examine all the samples at once, the complete solution files should be used. To build/examine a single sample, the individual sample solution files should be used.
> **Note:** Some samples require that the Microsoft DirectX SDK (June 2010 or newer) be installed and that the VC++ directory paths are properly set up (**Tools > Options...**). Check DirectX Dependencies section for details."
### Linux
The Linux samples are built using makefiles. To use the makefiles, change the current directory to the sample directory you wish to build, and run make:
```
$ cd <sample_dir>
$ make
```
The samples makefiles can take advantage of certain options:
* **TARGET_ARCH=<arch>** - cross-compile targeting a specific architecture. Allowed architectures are x86_64, ppc64le, armv7l.
By default, TARGET_ARCH is set to HOST_ARCH. On a x86_64 machine, not setting TARGET_ARCH is the equivalent of setting TARGET_ARCH=x86_64.<br/>
`$ make TARGET_ARCH=x86_64` <br/> `$ make TARGET_ARCH=ppc64le` <br/> `$ make TARGET_ARCH=armv7l` <br/>
See [here](http://docs.nvidia.com/cuda/cuda-samples/index.html#cross-samples) for more details.
* **dbg=1** - build with debug symbols
```
$ make dbg=1
```
* **SMS="A B ..."** - override the SM architectures for which the sample will be built, where `"A B ..."` is a space-delimited list of SM architectures. For example, to generate SASS for SM 50 and SM 60, use `SMS="50 60"`.
```
$ make SMS="50 60"
```
* **HOST_COMPILER=<host_compiler>** - override the default g++ host compiler. See the [Linux Installation Guide](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#system-requirements) for a list of supported host compilers.
```
$ make HOST_COMPILER=g++
```
## References (for more details)
[whitepaper](./doc/convolutionSeparable.pdf)

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/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#include <assert.h>
#include <helper_cuda.h>
#include <cooperative_groups.h>
namespace cg = cooperative_groups;
#include "convolutionSeparable_common.h"
////////////////////////////////////////////////////////////////////////////////
// Convolution kernel storage
////////////////////////////////////////////////////////////////////////////////
__constant__ float c_Kernel[KERNEL_LENGTH];
extern "C" void setConvolutionKernel(float *h_Kernel) {
cudaMemcpyToSymbol(c_Kernel, h_Kernel, KERNEL_LENGTH * sizeof(float));
}
////////////////////////////////////////////////////////////////////////////////
// Row convolution filter
////////////////////////////////////////////////////////////////////////////////
#define ROWS_BLOCKDIM_X 16
#define ROWS_BLOCKDIM_Y 4
#define ROWS_RESULT_STEPS 8
#define ROWS_HALO_STEPS 1
__global__ void convolutionRowsKernel(float *d_Dst, float *d_Src, int imageW,
int imageH, int pitch) {
// Handle to thread block group
cg::thread_block cta = cg::this_thread_block();
__shared__ float
s_Data[ROWS_BLOCKDIM_Y][(ROWS_RESULT_STEPS + 2 * ROWS_HALO_STEPS) *
ROWS_BLOCKDIM_X];
// Offset to the left halo edge
const int baseX =
(blockIdx.x * ROWS_RESULT_STEPS - ROWS_HALO_STEPS) * ROWS_BLOCKDIM_X +
threadIdx.x;
const int baseY = blockIdx.y * ROWS_BLOCKDIM_Y + threadIdx.y;
d_Src += baseY * pitch + baseX;
d_Dst += baseY * pitch + baseX;
// Load main data
#pragma unroll
for (int i = ROWS_HALO_STEPS; i < ROWS_HALO_STEPS + ROWS_RESULT_STEPS; i++) {
s_Data[threadIdx.y][threadIdx.x + i * ROWS_BLOCKDIM_X] =
d_Src[i * ROWS_BLOCKDIM_X];
}
// Load left halo
#pragma unroll
for (int i = 0; i < ROWS_HALO_STEPS; i++) {
s_Data[threadIdx.y][threadIdx.x + i * ROWS_BLOCKDIM_X] =
(baseX >= -i * ROWS_BLOCKDIM_X) ? d_Src[i * ROWS_BLOCKDIM_X] : 0;
}
// Load right halo
#pragma unroll
for (int i = ROWS_HALO_STEPS + ROWS_RESULT_STEPS;
i < ROWS_HALO_STEPS + ROWS_RESULT_STEPS + ROWS_HALO_STEPS; i++) {
s_Data[threadIdx.y][threadIdx.x + i * ROWS_BLOCKDIM_X] =
(imageW - baseX > i * ROWS_BLOCKDIM_X) ? d_Src[i * ROWS_BLOCKDIM_X] : 0;
}
// Compute and store results
cg::sync(cta);
#pragma unroll
for (int i = ROWS_HALO_STEPS; i < ROWS_HALO_STEPS + ROWS_RESULT_STEPS; i++) {
float sum = 0;
#pragma unroll
for (int j = -KERNEL_RADIUS; j <= KERNEL_RADIUS; j++) {
sum += c_Kernel[KERNEL_RADIUS - j] *
s_Data[threadIdx.y][threadIdx.x + i * ROWS_BLOCKDIM_X + j];
}
d_Dst[i * ROWS_BLOCKDIM_X] = sum;
}
}
extern "C" void convolutionRowsGPU(float *d_Dst, float *d_Src, int imageW,
int imageH) {
assert(ROWS_BLOCKDIM_X * ROWS_HALO_STEPS >= KERNEL_RADIUS);
assert(imageW % (ROWS_RESULT_STEPS * ROWS_BLOCKDIM_X) == 0);
assert(imageH % ROWS_BLOCKDIM_Y == 0);
dim3 blocks(imageW / (ROWS_RESULT_STEPS * ROWS_BLOCKDIM_X),
imageH / ROWS_BLOCKDIM_Y);
dim3 threads(ROWS_BLOCKDIM_X, ROWS_BLOCKDIM_Y);
convolutionRowsKernel<<<blocks, threads>>>(d_Dst, d_Src, imageW, imageH,
imageW);
getLastCudaError("convolutionRowsKernel() execution failed\n");
}
////////////////////////////////////////////////////////////////////////////////
// Column convolution filter
////////////////////////////////////////////////////////////////////////////////
#define COLUMNS_BLOCKDIM_X 16
#define COLUMNS_BLOCKDIM_Y 8
#define COLUMNS_RESULT_STEPS 8
#define COLUMNS_HALO_STEPS 1
__global__ void convolutionColumnsKernel(float *d_Dst, float *d_Src, int imageW,
int imageH, int pitch) {
// Handle to thread block group
cg::thread_block cta = cg::this_thread_block();
__shared__ float s_Data[COLUMNS_BLOCKDIM_X][(COLUMNS_RESULT_STEPS +
2 * COLUMNS_HALO_STEPS) *
COLUMNS_BLOCKDIM_Y +
1];
// Offset to the upper halo edge
const int baseX = blockIdx.x * COLUMNS_BLOCKDIM_X + threadIdx.x;
const int baseY = (blockIdx.y * COLUMNS_RESULT_STEPS - COLUMNS_HALO_STEPS) *
COLUMNS_BLOCKDIM_Y +
threadIdx.y;
d_Src += baseY * pitch + baseX;
d_Dst += baseY * pitch + baseX;
// Main data
#pragma unroll
for (int i = COLUMNS_HALO_STEPS;
i < COLUMNS_HALO_STEPS + COLUMNS_RESULT_STEPS; i++) {
s_Data[threadIdx.x][threadIdx.y + i * COLUMNS_BLOCKDIM_Y] =
d_Src[i * COLUMNS_BLOCKDIM_Y * pitch];
}
// Upper halo
#pragma unroll
for (int i = 0; i < COLUMNS_HALO_STEPS; i++) {
s_Data[threadIdx.x][threadIdx.y + i * COLUMNS_BLOCKDIM_Y] =
(baseY >= -i * COLUMNS_BLOCKDIM_Y)
? d_Src[i * COLUMNS_BLOCKDIM_Y * pitch]
: 0;
}
// Lower halo
#pragma unroll
for (int i = COLUMNS_HALO_STEPS + COLUMNS_RESULT_STEPS;
i < COLUMNS_HALO_STEPS + COLUMNS_RESULT_STEPS + COLUMNS_HALO_STEPS;
i++) {
s_Data[threadIdx.x][threadIdx.y + i * COLUMNS_BLOCKDIM_Y] =
(imageH - baseY > i * COLUMNS_BLOCKDIM_Y)
? d_Src[i * COLUMNS_BLOCKDIM_Y * pitch]
: 0;
}
// Compute and store results
cg::sync(cta);
#pragma unroll
for (int i = COLUMNS_HALO_STEPS;
i < COLUMNS_HALO_STEPS + COLUMNS_RESULT_STEPS; i++) {
float sum = 0;
#pragma unroll
for (int j = -KERNEL_RADIUS; j <= KERNEL_RADIUS; j++) {
sum += c_Kernel[KERNEL_RADIUS - j] *
s_Data[threadIdx.x][threadIdx.y + i * COLUMNS_BLOCKDIM_Y + j];
}
d_Dst[i * COLUMNS_BLOCKDIM_Y * pitch] = sum;
}
}
extern "C" void convolutionColumnsGPU(float *d_Dst, float *d_Src, int imageW,
int imageH) {
assert(COLUMNS_BLOCKDIM_Y * COLUMNS_HALO_STEPS >= KERNEL_RADIUS);
assert(imageW % COLUMNS_BLOCKDIM_X == 0);
assert(imageH % (COLUMNS_RESULT_STEPS * COLUMNS_BLOCKDIM_Y) == 0);
dim3 blocks(imageW / COLUMNS_BLOCKDIM_X,
imageH / (COLUMNS_RESULT_STEPS * COLUMNS_BLOCKDIM_Y));
dim3 threads(COLUMNS_BLOCKDIM_X, COLUMNS_BLOCKDIM_Y);
convolutionColumnsKernel<<<blocks, threads>>>(d_Dst, d_Src, imageW, imageH,
imageW);
getLastCudaError("convolutionColumnsKernel() execution failed\n");
}

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/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef CONVOLUTIONSEPARABLE_COMMON_H
#define CONVOLUTIONSEPARABLE_COMMON_H
#define KERNEL_RADIUS 8
#define KERNEL_LENGTH (2 * KERNEL_RADIUS + 1)
////////////////////////////////////////////////////////////////////////////////
// Reference CPU convolution
////////////////////////////////////////////////////////////////////////////////
extern "C" void convolutionRowCPU(float *h_Dst, float *h_Src, float *h_Kernel,
int imageW, int imageH, int kernelR);
extern "C" void convolutionColumnCPU(float *h_Dst, float *h_Src,
float *h_Kernel, int imageW, int imageH,
int kernelR);
////////////////////////////////////////////////////////////////////////////////
// GPU convolution
////////////////////////////////////////////////////////////////////////////////
extern "C" void setConvolutionKernel(float *h_Kernel);
extern "C" void convolutionRowsGPU(float *d_Dst, float *d_Src, int imageW,
int imageH);
extern "C" void convolutionColumnsGPU(float *d_Dst, float *d_Src, int imageW,
int imageH);
#endif

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/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#include "convolutionSeparable_common.h"
////////////////////////////////////////////////////////////////////////////////
// Reference row convolution filter
////////////////////////////////////////////////////////////////////////////////
extern "C" void convolutionRowCPU(float *h_Dst, float *h_Src, float *h_Kernel,
int imageW, int imageH, int kernelR) {
for (int y = 0; y < imageH; y++)
for (int x = 0; x < imageW; x++) {
float sum = 0;
for (int k = -kernelR; k <= kernelR; k++) {
int d = x + k;
if (d >= 0 && d < imageW)
sum += h_Src[y * imageW + d] * h_Kernel[kernelR - k];
}
h_Dst[y * imageW + x] = sum;
}
}
////////////////////////////////////////////////////////////////////////////////
// Reference column convolution filter
////////////////////////////////////////////////////////////////////////////////
extern "C" void convolutionColumnCPU(float *h_Dst, float *h_Src,
float *h_Kernel, int imageW, int imageH,
int kernelR) {
for (int y = 0; y < imageH; y++)
for (int x = 0; x < imageW; x++) {
float sum = 0;
for (int k = -kernelR; k <= kernelR; k++) {
int d = y + k;
if (d >= 0 && d < imageH)
sum += h_Src[d * imageW + x] * h_Kernel[kernelR - k];
}
h_Dst[y * imageW + x] = sum;
}
}

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Microsoft Visual Studio Solution File, Format Version 12.00
# Visual Studio 2017
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{997E0757-EA74-4A4E-A0FC-47D8C8831A15}.Release|x64.ActiveCfg = Release|x64
{997E0757-EA74-4A4E-A0FC-47D8C8831A15}.Release|x64.Build.0 = Release|x64
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Microsoft Visual Studio Solution File, Format Version 12.00
# Visual Studio 2019
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{997E0757-EA74-4A4E-A0FC-47D8C8831A15}.Debug|x64.ActiveCfg = Debug|x64
{997E0757-EA74-4A4E-A0FC-47D8C8831A15}.Debug|x64.Build.0 = Debug|x64
{997E0757-EA74-4A4E-A0FC-47D8C8831A15}.Release|x64.ActiveCfg = Release|x64
{997E0757-EA74-4A4E-A0FC-47D8C8831A15}.Release|x64.Build.0 = Release|x64
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<PropertyGroup Label="Globals">
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Microsoft Visual Studio Solution File, Format Version 12.00
# Visual Studio 2022
Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "convolutionSeparable", "convolutionSeparable_vs2022.vcxproj", "{997E0757-EA74-4A4E-A0FC-47D8C8831A15}"
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Debug|x64 = Debug|x64
Release|x64 = Release|x64
EndGlobalSection
GlobalSection(ProjectConfigurationPlatforms) = postSolution
{997E0757-EA74-4A4E-A0FC-47D8C8831A15}.Debug|x64.ActiveCfg = Debug|x64
{997E0757-EA74-4A4E-A0FC-47D8C8831A15}.Debug|x64.Build.0 = Debug|x64
{997E0757-EA74-4A4E-A0FC-47D8C8831A15}.Release|x64.ActiveCfg = Release|x64
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</CudaCompile>
</ItemDefinitionGroup>
<ItemDefinitionGroup Condition="'$(Configuration)'=='Release'">
<ClCompile>
<Optimization>MaxSpeed</Optimization>
<RuntimeLibrary>MultiThreaded</RuntimeLibrary>
</ClCompile>
<Link>
<GenerateDebugInformation>false</GenerateDebugInformation>
<LinkTimeCodeGeneration>UseLinkTimeCodeGeneration</LinkTimeCodeGeneration>
</Link>
<CudaCompile>
<Runtime>MT</Runtime>
<TargetMachinePlatform>64</TargetMachinePlatform>
</CudaCompile>
</ItemDefinitionGroup>
<ItemGroup>
<CudaCompile Include="convolutionSeparable.cu" />
<ClCompile Include="convolutionSeparable_gold.cpp" />
<ClCompile Include="main.cpp" />
<ClInclude Include="convolutionSeparable_common.h" />
</ItemGroup>
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
<ImportGroup Label="ExtensionTargets">
<Import Project="$(CUDAPropsPath)\CUDA 11.6.targets" />
</ImportGroup>
</Project>

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/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
/*
* This sample implements a separable convolution filter
* of a 2D image with an arbitrary kernel.
*/
// CUDA runtime
#include <cuda_runtime.h>
// Utilities and system includes
#include <helper_functions.h>
#include <helper_cuda.h>
#include "convolutionSeparable_common.h"
////////////////////////////////////////////////////////////////////////////////
// Reference CPU convolution
////////////////////////////////////////////////////////////////////////////////
extern "C" void convolutionRowCPU(float *h_Result, float *h_Data,
float *h_Kernel, int imageW, int imageH,
int kernelR);
extern "C" void convolutionColumnCPU(float *h_Result, float *h_Data,
float *h_Kernel, int imageW, int imageH,
int kernelR);
////////////////////////////////////////////////////////////////////////////////
// Main program
////////////////////////////////////////////////////////////////////////////////
int main(int argc, char **argv) {
// start logs
printf("[%s] - Starting...\n", argv[0]);
float *h_Kernel, *h_Input, *h_Buffer, *h_OutputCPU, *h_OutputGPU;
float *d_Input, *d_Output, *d_Buffer;
const int imageW = 3072;
const int imageH = 3072;
const int iterations = 16;
StopWatchInterface *hTimer = NULL;
// Use command-line specified CUDA device, otherwise use device with highest
// Gflops/s
findCudaDevice(argc, (const char **)argv);
sdkCreateTimer(&hTimer);
printf("Image Width x Height = %i x %i\n\n", imageW, imageH);
printf("Allocating and initializing host arrays...\n");
h_Kernel = (float *)malloc(KERNEL_LENGTH * sizeof(float));
h_Input = (float *)malloc(imageW * imageH * sizeof(float));
h_Buffer = (float *)malloc(imageW * imageH * sizeof(float));
h_OutputCPU = (float *)malloc(imageW * imageH * sizeof(float));
h_OutputGPU = (float *)malloc(imageW * imageH * sizeof(float));
srand(200);
for (unsigned int i = 0; i < KERNEL_LENGTH; i++) {
h_Kernel[i] = (float)(rand() % 16);
}
for (unsigned i = 0; i < imageW * imageH; i++) {
h_Input[i] = (float)(rand() % 16);
}
printf("Allocating and initializing CUDA arrays...\n");
checkCudaErrors(
cudaMalloc((void **)&d_Input, imageW * imageH * sizeof(float)));
checkCudaErrors(
cudaMalloc((void **)&d_Output, imageW * imageH * sizeof(float)));
checkCudaErrors(
cudaMalloc((void **)&d_Buffer, imageW * imageH * sizeof(float)));
setConvolutionKernel(h_Kernel);
checkCudaErrors(cudaMemcpy(d_Input, h_Input, imageW * imageH * sizeof(float),
cudaMemcpyHostToDevice));
printf("Running GPU convolution (%u identical iterations)...\n\n",
iterations);
for (int i = -1; i < iterations; i++) {
// i == -1 -- warmup iteration
if (i == 0) {
checkCudaErrors(cudaDeviceSynchronize());
sdkResetTimer(&hTimer);
sdkStartTimer(&hTimer);
}
convolutionRowsGPU(d_Buffer, d_Input, imageW, imageH);
convolutionColumnsGPU(d_Output, d_Buffer, imageW, imageH);
}
checkCudaErrors(cudaDeviceSynchronize());
sdkStopTimer(&hTimer);
double gpuTime = 0.001 * sdkGetTimerValue(&hTimer) / (double)iterations;
printf(
"convolutionSeparable, Throughput = %.4f MPixels/sec, Time = %.5f s, "
"Size = %u Pixels, NumDevsUsed = %i, Workgroup = %u\n",
(1.0e-6 * (double)(imageW * imageH) / gpuTime), gpuTime,
(imageW * imageH), 1, 0);
printf("\nReading back GPU results...\n\n");
checkCudaErrors(cudaMemcpy(h_OutputGPU, d_Output,
imageW * imageH * sizeof(float),
cudaMemcpyDeviceToHost));
printf("Checking the results...\n");
printf(" ...running convolutionRowCPU()\n");
convolutionRowCPU(h_Buffer, h_Input, h_Kernel, imageW, imageH, KERNEL_RADIUS);
printf(" ...running convolutionColumnCPU()\n");
convolutionColumnCPU(h_OutputCPU, h_Buffer, h_Kernel, imageW, imageH,
KERNEL_RADIUS);
printf(" ...comparing the results\n");
double sum = 0, delta = 0;
for (unsigned i = 0; i < imageW * imageH; i++) {
delta +=
(h_OutputGPU[i] - h_OutputCPU[i]) * (h_OutputGPU[i] - h_OutputCPU[i]);
sum += h_OutputCPU[i] * h_OutputCPU[i];
}
double L2norm = sqrt(delta / sum);
printf(" ...Relative L2 norm: %E\n\n", L2norm);
printf("Shutting down...\n");
checkCudaErrors(cudaFree(d_Buffer));
checkCudaErrors(cudaFree(d_Output));
checkCudaErrors(cudaFree(d_Input));
free(h_OutputGPU);
free(h_OutputCPU);
free(h_Buffer);
free(h_Input);
free(h_Kernel);
sdkDeleteTimer(&hTimer);
if (L2norm > 1e-6) {
printf("Test failed!\n");
exit(EXIT_FAILURE);
}
printf("Test passed\n");
exit(EXIT_SUCCESS);
}