I have talked about texture memory in CUDA in this article previously. In the previous article, we were talking about Texture Reference. Now in this article I’m introducing new feature of Kepler architecture called Texture object.
For this, we recap some of the important concept of texture memory that we have discussed in details in this article previously. Feel free to brows this article for more theory and example.
Texture memory is read from kernels using the device functions described in Texture Functions. The process of reading a texture calling one of these functions is called a texture fetch. Each texture fetch specifies a parameter called a texture object for the texture object API or a texture reference for the texture reference API.
Texture which is the piece of texture memory that is fetched. Texture objects are created at runtime and the texture is specified when creating the texture object as described in Texture Object API. Texture references are created at compile time and the texture is specified at runtime by bounding the texture reference to the texture through runtime functions as described in Texture Reference API; several distinct texture references might be bound to the same texture or to textures that overlap in memory. A texture can be any region of linear memory or a CUDA array (described in CUDA Arrays).
Its dimensionality that specifies whether the texture is addressed as a one dimensional array using one texture coordinate, a two-dimensional array using two texture coordinates, or a three-dimensional array using three texture coordinates. Elements of the array are called texels, short for texture elements. The texture width, height, and depth refer to the size of the array in each dimension. Table Wiki lists the maximum texture width, height, and depth depending on the compute capability of the device.
TEXTURE type (Texel)
The type of a texel, which is restricted to the basic integer and single-precision floating-point types and any of the 1-, 2-, and 4-component vector types defined in char, short, int, long, long long, float, double.
TEXTURE Read Mode
The read mode, which is equal to cudaReadModeNormalizedFloat or cudaReadModeElementType. If it is cudaReadModeNormalizedFloat and the type of the texel is a 16-bit or 8-bit integer type, the value returned by the texture fetch is actually returned as floating-point type and the full range of the integer type is mapped to [0.0, 1.0] for unsigned integer type and [-1.0, 1.0] for signed integer type; for example, an unsigned 8-bit texture element with the value 0xff reads as 1. If it is
cudaReadModeElementType, no conversion is performed.
TEXTURE Normalized coordinates
Whether texture coordinates are normalized or not. By default, textures are referenced (by the functions of Texture Functions) using floating-point coordinates in the range [0, N-1] where N is the size of the texture in the dimension corresponding to the coordinate. For example, a texture that is 64x32 in size will be referenced with coordinates in the range [0, 63] and [0, 31] for the x and y dimensions, respectively. Normalized texture coordinates cause the coordinates to be specified in the range [0.0, 1.0-1/N] instead of [0, N-1], so the same 64x32 texture would be addressed by normalized coordinates in the range [0, 1-1/N] in both the x and y dimensions. Normalized texture coordinates are a natural fit to some applications' requirements, if it is preferable for the texture coordinates to be independent of the texture size.
TEXTURE addressing mode
It is valid to call the device functions with coordinates that are out of range. The addressing mode defines what happens in that case. The default addressing mode is to clamp the coordinates to the valid range: [0, N) for non-normalized coordinates and [0.0, 1.0) for normalized coordinates. If the border mode is specified instead, texture fetches with out-of-range texture coordinates return zero. For normalized coordinates, the warp mode and the mirror mode are also available. When using the wrap mode, each coordinate x is converted to frac(x)= x floor(x) where floor(x) is the largest integer not greater than x. When using the mirror mode, each coordinate x is converted to frac(x) if floor(x) is even and 1-frac(x) if floor(x) is odd. The addressing mode is specified as an array of size three whose first, second, and third elements specify the addressing mode for the first, second, and third texture coordinates, respectively; the addressing mode are cudaAddressModeBorder, cudaAddressModeClamp, cudaAddressModeWrap and cudaAddressModeMirror; cudaAddressModeWrap and
cudaAddressModeMirror are only supported for normalized texture coordinates.
TEXTURE filtering mode
The filtering mode which specifies how the value returned when fetching the texture is computed based on the input texture coordinates. Linear texture filtering may be done only for textures that are configured to return floating-point data. It performs
Low-precision interpolation between neighboring texels. When enabled, the texels surrounding a texture fetch location are read and the return value of the texture fetch is interpolated based on where the texture coordinates fell between the texels. Simple linear interpolation is performed for one-dimensional textures, bilinear interpolation for two-dimensional textures, and trilinear interpolation for three-dimensional textures. Texture Fetching gives more details on texture fetching. The filtering mode is equal to cudaFilterModePoint or cudaFilterModeLinear. If it is cudaFilterModePoint, the returned value is the texel whose texture coordinates are the closest to the input texture coordinates. If it is cudaFilterModeLinear, the returned value is the linear interpolation of the two (for a one-dimensional texture), four (for a two dimensional texture), or eight (for a three dimensional texture) texels whose texture coordinates are the closest to the input texture coordinates. cudaFilterModeLinear is only valid for returned values of floating-point type.
TEXTURE object api
A texture object is created using cudaCreateTextureObject() from a resource description of type struct cudaResourceDesc, which specifies the texture, and from a texture description defined as such:
enum cudaTextureAddressMode addressMode;
enum cudaTextureFilterMode filterMode;
enum cudaTextureReadMode readMode;
unsigned int maxAnisotropy;
enum cudaTextureFilterMode mipmapFilterMode;
· addressMode specifies the addressing mode;
· filterMode specifies the filter mode;
· readMode specifies the read mode;
· normalizedCoords specifies whether texture coordinates are normalized or not;
Textures are likely a familiar concept to anyone who’s done much CUDA programming. A feature from the graphics world, textures are images that are stretched, rotated and pasted on polygons to form the 3D graphics we are familiar with. Using textures for GPU computing has always been a pro tip for the CUDA programmer; they enable fast random access to arrays and use a cache to provide bandwidth aggregation. On the flip side, the legacy texture reference API is cumbersome to use because it requires manual binding and unbinding of texture references to memory addresses, as the following code demonstrates. Also, texture references can only be declared as static global variables and cannot be passed as function arguments.
Kepler GPUs and CUDA 5.0 introduce a new feature called texture objects (sometimes called , since they don’t require manual binding/unbinding) that greatly improves the usability and programmability of textures. Texture objects use the new cudaTextureObject_t class API, whereby textures become first-class C++ objects and can be passed as arguments just as if they were pointers. There is no need to know at compile time which textures will be used at run time, which enables much more dynamic execution and flexible programming, as shown in the following code.
Moreover, texture objects only need to be instantiated once, and are not subject to the hardware limit of 128 texture references, so there is no need to continuously bind and unbind them. (The Kepler hardware limit is over one million texture objects, a limit that will likely never be reached by any practical CUDA application in the lifetime of the architecture!) Using texture objects, the overhead of binding (up to 1 μs) and unbinding (up to 0.5 μs) textures is eliminated. What is not commonly known is that each outstanding texture reference that is bound when a kernel is launched incurs added launch latency—up to 0.5 μs per texture reference. This launch overhead persists even if the outstanding bound textures are not even referenced by the kernel. Again, using texture objects instead of texture references completely removes this overhead.
STRONG SCALING IN HPC
While these overheads may sound minor for any application that uses kernels that run for milliseconds or greater in duration, they are important for latency-sensitive, short-running kernels. High Performance Computing (HPC) applications that need strong scaling typify this use case. Strong scaling is how the solution time varies with the number of processors for a fixed global problem size. The goal is to solve a fixed problem in as short a time as possible. There is usually a limit to strong scaling, as at some point an application will become bound by the inter-processor communication bandwidth or latencies that arise from doing less work on each processor. Beyond this limit the total performance will actually decrease.
Figure 1: Strong-scaling performance of the QUDA solver using both texture references and texture objects.
There are many large-scale HPC applications that will be running on the Titan supercomputer at Oak Ridge National Laboratory in Tennessee, for which achieving excellent strong scaling performance will be vital. Lattice Quantum Chromodynamics (LQCD) is one such application. LQCD uses a combination of sparse linear solvers, molecular dynamics algorithms and Monte Carlo methods to probe the structure of the nucleus. Using the highly optimized QUDA library, legacy LQCD applications MILC and Chroma can run on Titan making full use of the attached Tesla K20X accelerators. The QUDA library makes extensive use of textures, and using texture references the strong scaling tops out at 2 TFLOP/s, as you can see in Figure 1. Profiling using the CUDA visual profiler revealed that the overhead of texture binding and unbinding contributed significantly to time-critical communication routines running on the GPU. Moreover, many of the kernels run for O(10μs) using up to ten textures simultaneously. Thus the launch latency overhead is a significant bottleneck. Once the library was rewritten to use texture objects, strong scaling performance increased significantly and throughput increased to nearly 3 TFLOP/s at 27 GPUs (see Figure 1).
Texture objects are yet another powerful feature of the Kepler architecture that make it easier to achieve high performance and scalability with CUDA.
// Simple transformation kernel
__global__ void transformKernel(float* output, cudaTextureObject_t texObj, int width, int height, float theta)
// Calculate normalized texture coordinates
unsigned int x = blockIdx.x * blockDim.x + threadIdx.x;
unsigned int y = blockIdx.y * blockDim.y + threadIdx.y;
float u = x / (float)width;
float v = y / (float)height;
// Transform coordinates
u -= 0.5f;
v -= 0.5f;
float tu = u * cosf(theta) – v * sinf(theta) + 0.5f;
float tv = v * cosf(theta) + u * sinf(theta) + 0.5f;
// Read from texture and write to global memory
output[y * width + x] = tex2D<float>(texObj, tu, tv);
// Host code
// Allocate CUDA array in device memory
cudaChannelFormatDesc channelDesc =cudaCreateChannelDesc(32, 0, 0, 0, cudaChannelFormatKindFloat);
cudaMallocArray(&cuArray, &channelDesc, width, height);
// Copy to device memory some data located at address h_data
// in host memory
cudaMemcpyToArray(cuArray, 0, 0, h_data, size,cudaMemcpyHostToDevice);
// Specify texture
struct cudaResourceDesc resDesc;
memset(&resDesc, 0, sizeof(resDesc));
resDesc.resType = cudaResourceTypeArray;
resDesc.res.array.array = cuArray;
// Specify texture object parameters
struct cudaTextureDesc texDesc;
memset(&texDesc, 0, sizeof(texDesc));
texDesc.addressMode = cudaAddressModeWrap;
texDesc.addressMode = cudaAddressModeWrap;
texDesc.filterMode = cudaFilterModeLinear;
texDesc.readMode = cudaReadModeElementType;
texDesc.normalizedCoords = 1;
// Create texture object
cudaTextureObject_t texObj = 0;
cudaCreateTextureObject(&texObj, &resDesc, &texDesc, NULL);
// Allocate result of transformation in device memory
cudaMalloc(&output, width * height * sizeof(float));
// Invoke kernel
dim3 dimGrid((width + dimBlock.x – 1) / dimBlock.x, (height + dimBlock.y – 1) / dimBlock.y);
transformKernel<<<dimGrid, dimBlock>>>(output, texObj, width, height, angle);
// Destroy texture object
// Free device memory
CUDA C programming Guide