TO THE EXTENT NOT PROHIBITED BY LAW, IN NO EVENT WILL NVIDIA BE LIABLE FOR ANY DAMAGES, INCLUDING WITHOUT LIMITATION ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, PUNITIVE, OR CONSEQUENTIAL DAMAGES, HOWEVER CAUSED AND REGARDLESS OF THE THEORY OF LIABILITY, ARISING OUT OF ANY USE OF THIS DOCUMENT, EVEN IF NVIDIA HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. Non-default streams are required for this overlap because memory copy, memory set functions, and kernel calls that use the default stream begin only after all preceding calls on the device (in any stream) have completed, and no operation on the device (in any stream) commences until they are finished. For exponentiation using base 2 or 10, use the functions exp2() or expf2() and exp10() or expf10() rather than the functions pow() or powf(). The kernel is executed within a loop in host code that varies the parameter offset from 0 to 32. The host code in Zero-copy host code shows how zero copy is typically set up. C++-style convenience wrappers (cuda_runtime.h) built on top of the C-style functions. An application has no direct control over these bank conflicts. For best performance, there should be some coherence in memory access by adjacent threads running on the device. Shared memory is a powerful feature for writing well optimized CUDA code. Furthermore, this file should be installed into the @rpath of the application; see Where to Install Redistributed CUDA Libraries. THIS DOCUMENT AND ALL NVIDIA DESIGN SPECIFICATIONS, REFERENCE BOARDS, FILES, DRAWINGS, DIAGNOSTICS, LISTS, AND OTHER DOCUMENTS (TOGETHER AND SEPARATELY, MATERIALS) ARE BEING PROVIDED AS IS. NVIDIA MAKES NO WARRANTIES, EXPRESSED, IMPLIED, STATUTORY, OR OTHERWISE WITH RESPECT TO THE MATERIALS, AND EXPRESSLY DISCLAIMS ALL IMPLIED WARRANTIES OF NONINFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE. Finally, particular attention must be paid to control flow instructions due to the SIMT (single instruction multiple thread) nature of the device. The CUDA Toolkit includes a number of such libraries that have been fine-tuned for NVIDIA CUDA GPUs, such as cuBLAS, cuFFT, and so on. This is done by the nvcc compiler when it determines that there is insufficient register space to hold the variable. In such cases, and when the execution time (tE) exceeds the transfer time (tT), a rough estimate for the overall time is tE + tT/nStreams for the staged version versus tE + tT for the sequential version. The output for that program is shown in Figure 16. Users should refer to the CUDA headers and documentation for new CUDA APIs introduced in a release. When using NVRTC, it is recommended that the resulting PTX code is first transformed to the final device code via the steps outlined by the PTX user workflow. The types of operations are an additional factor, as additions have different complexity profiles than, for example, trigonometric functions. There is no way to check this for a specific variable, but the compiler reports total local memory usage per kernel (lmem) when run with the--ptxas-options=-v option. After each round of application parallelization is complete, the developer can move to optimizing the implementation to improve performance. Devices to be made visible to the application should be included as a comma-separated list in terms of the system-wide list of enumerable devices. (The exceptions to this are kernel launches, which return void, and cudaGetErrorString(), which returns a character string describing the cudaError_t code that was passed into it.) This can be configured during runtime API from the host for all kernelsusing cudaDeviceSetCacheConfig() or on a per-kernel basis using cudaFuncSetCacheConfig(). As can be seen from these tables, judicious use of shared memory can dramatically improve performance. This also prevents array elements being repeatedly read from global memory if the same data is required several times. Verify that your library doesnt leak dependencies, breakages, namespaces, etc. An application that exhibits linear strong scaling has a speedup equal to the number of processors used. This is possible because the distribution of the warps across the block is deterministic as mentioned in SIMT Architecture of the CUDA C++ Programming Guide. Like Volta, the NVIDIA Ampere GPU architecture combines the functionality of the L1 and texture caches into a unified L1/Texture cache which acts as a coalescing buffer for memory accesses, gathering up the data requested by the threads of a warp prior to delivery of that data to the warp. The following complete code (available on GitHub) illustrates various methods of using shared memory. A CUDA device has a number of different memory components that are available to programmers - register, shared memory, local memory, global memory and constant memory. A further improvement can be made to how Using shared memory to improve the global memory load efficiency in matrix multiplication deals with matrix B. For more information on the Arrive/Wait Barriers refer to the Arrive/Wait Barrier section in the CUDA C++ Programming Guide. Register storage enables threads to keep local variables nearby for low-latency access. Threads on a CPU are generally heavyweight entities. Kernel access to global memory also should be minimized by maximizing the use of shared memory on the device. Accesses to different addresses by threads within a warp are serialized, thus the cost scales linearly with the number of unique addresses read by all threads within a warp. For some fractional exponents, exponentiation can be accelerated significantly compared to the use of pow() by using square roots, cube roots, and their inverses. To scale to future devices, the number of blocks per kernel launch should be in the thousands. The following examples use the cuBLAS library from CUDA Toolkit 5.5 as an illustration: In a shared library on Linux, there is a string field called the SONAME that indicates the binary compatibility level of the library. APIs can be deprecated and removed. In this case the shared memory allocation size per thread block must be specified (in bytes) using an optional third execution configuration parameter, as in the following excerpt. The new Tensor Cores use a larger base matrix size and add powerful new math modes including: Support for FP64 Tensor Core, using new DMMA instructions. Devices of compute capability 2.0 and later support a special addressing mode called Unified Virtual Addressing (UVA) on 64-bit Linux and Windows. For example, if you link against the CUDA 11.1 dynamic runtime, and use functionality from 11.1, as well as a separate shared library that was linked against the CUDA 11.2 dynamic runtime that requires 11.2 functionality, the final link step must include a CUDA 11.2 or newer dynamic runtime. Before tackling other hotspots to improve the total speedup, the developer should consider taking the partially parallelized implementation and carry it through to production. The CUDA Toolkit libraries (cuBLAS, cuFFT, etc.) This variant simply uses the transpose of A in place of B, so C = AAT. Data Transfer Between Host and Device provides further details, including the measurements of bandwidth between the host and the device versus within the device proper. Now Let's Look at Shared Memory Common Programming Pattern (5.1.2 of CUDA manual) - Load data into shared memory - Synchronize (if necessary) - Operate on data in shared memory - Synchronize (if necessary) - Write intermediate results to global memory - Repeat until done Shared memory Global memory Familiar concept?? Generally, accessing a register consumes zero extra clock cycles per instruction, but delays may occur due to register read-after-write dependencies and register memory bank conflicts. Low Priority: Make it easy for the compiler to use branch predication in lieu of loops or control statements. For example, to compute the effective bandwidth of a 2048 x 2048 matrix copy, the following formula could be used: \(\text{Effective\ bandwidth} = \left( {\left( 2048^{2} \times 4 \times 2 \right) \div 10^{9}} \right) \div \text{time}\). The maximum number of concurrent warps per SM remains the same as in Volta (i.e., 64), and other factors influencing warp occupancy are: The register file size is 64K 32-bit registers per SM. Improvement by reading additional data into shared memory. These are the same contexts used implicitly by the CUDA Runtime when there is not already a current context for a thread. Here, the effective bandwidth is in units of GB/s, Br is the number of bytes read per kernel, Bw is the number of bytes written per kernel, and time is given in seconds. Shared memory Bank Conflicts: Shared memory bank conflicts exist and are common for the strategy used. As a result, all modern processors require parallel code in order to achieve good utilization of their computational power. Always check the error return values on all CUDA API functions, even for functions that are not expected to fail, as this will allow the application to detect and recover from errors as soon as possible should they occur. A shared memory request for a warp is not split as with devices of compute capability 1.x, meaning that bank conflicts can occur between threads in the first half of a warp and threads in the second half of the same warp. High Priority: Minimize data transfer between the host and the device, even if it means running some kernels on the device that do not show performance gains when compared with running them on the host CPU. The NVIDIA nvcc compiler driver converts .cu files into C++ for the host system and CUDA assembly or binary instructions for the device. OpenCL is a trademark of Apple Inc. used under license to the Khronos Group Inc. NVIDIA and the NVIDIA logo are trademarks or registered trademarks of NVIDIA Corporation in the U.S. and other countries. It is therefore best to redistribute the CUDA Runtime library with the application when using dynamic linking or else to statically link against the CUDA Runtime. However, if multiple threads requested addresses map to the same memory bank, the accesses are serialized. Consider a simple transpose of a [2048, 1024] matrix to [1024, 2048]. There's no way around this. For devices of compute capability 2.0, the warp size is 32 threads and the number of banks is also 32. It would have been more so if adjacent warps had not exhibited such a high degree of reuse of the over-fetched cache lines. In this code, the canMapHostMemory field of the structure returned by cudaGetDeviceProperties() is used to check that the device supports mapping host memory to the devices address space. The performance of the sliding-window benchmark with tuned hit-ratio. As an exception, scattered writes to HBM2 see some overhead from ECC but much less than the overhead with similar access patterns on ECC-protected GDDR5 memory. Mapped pinned host memory allows you to overlap CPU-GPU memory transfers with computation while avoiding the use of CUDA streams. See Hardware Multithreading of the CUDA C++ Programming Guide for the register allocation formulas for devices of various compute capabilities and Features and Technical Specifications of the CUDA C++ Programming Guide for the total number of registers available on those devices. It is limited. To view a librarys install name, use the otool -L command: The binary compatibility version of the CUDA libraries on Windows is indicated as part of the filename. The NVIDIA Ampere GPU architecture allows CUDA users to control the persistence of data in L2 cache. Since shared memory is shared amongst threads in a thread block, it provides a mechanism for threads to cooperate. If the shared memory array size is known at compile time, as in the staticReverse kernel, then we can explicitly declare an array of that size, as we do with the array s. In this kernel, t and tr are the two indices representing the original and reverse order, respectively. The number of blocks in a grid should be larger than the number of multiprocessors so that all multiprocessors have at least one block to execute. Understanding the Programming Environment, 15. We evaluate the performance of both kernels using elements of size 4B, 8B and 16B per thread i.e., using int, int2 and int4 for the template parameter. This makes the code run faster at the cost of diminished precision and accuracy. We want to ensure that each change we make is correct and that it improves performance (and by how much). Is it possible to share a Cuda context between applications - Introduction CUDA is a parallel computing platform and programming model created by Nvidia. This approach permits some overlapping of the data transfer and execution. To enable the loads from global memory to be coalesced, data are read from global memory sequentially. There are many possible approaches to profiling the code, but in all cases the objective is the same: to identify the function or functions in which the application is spending most of its execution time. As the host and device memories are separated, items in the host memory must occasionally be communicated between device memory and host memory as described in What Runs on a CUDA-Enabled Device?. Functions following functionName() naming convention are slower but have higher accuracy (e.g., sinf(x) and expf(x)). This guide introduces the Assess, Parallelize, Optimize, Deploy(APOD) design cycle for applications with the goal of helping application developers to rapidly identify the portions of their code that would most readily benefit from GPU acceleration, rapidly realize that benefit, and begin leveraging the resulting speedups in production as early as possible. In CUDA there is no defined global synchronization mechanism except the kernel launch. Copy the results from device memory to host memory, also called device-to-host transfer. likewise return their own sets of error codes. Its important to be aware that calling __syncthreads() in divergent code is undefined and can lead to deadlockall threads within a thread block must call __syncthreads() at the same point. The cudaEventElapsedTime() function returns the time elapsed between the recording of the start and stop events. This approach will tend to provide the best results for the time invested and will avoid the trap of premature optimization. These examples assume compute capability 6.0 or higher and that accesses are for 4-byte words, unless otherwise noted. To prevent the compiler from allocating too many registers, use the -maxrregcount=N compiler command-line option (see nvcc) or the launch bounds kernel definition qualifier (see Execution Configuration of the CUDA C++ Programming Guide) to control the maximum number of registers to allocated per thread. For some applications the problem size will remain constant and hence only strong scaling is applicable. Furthermore, there should be multiple active blocks per multiprocessor so that blocks that arent waiting for a __syncthreads() can keep the hardware busy. However, based on what you've described here, your algorithm might be amenable to an approach similar to what is outlined in the threadfence reduction sample. The CUDA event API provides calls that create and destroy events, record events (including a timestamp), and convert timestamp differences into a floating-point value in milliseconds. Constantly recompiling with the latest CUDA Toolkit means forcing upgrades on the end-customers of an application product. Current GPUs can simultaneously process asynchronous data transfers and execute kernels. The goal is to maximize the use of the hardware by maximizing bandwidth. Using these data items, the peak theoretical memory bandwidth of the NVIDIA Tesla V100 is 898 GB/s: \(\left. Use of such information may require a license from a third party under the patents or other intellectual property rights of the third party, or a license from NVIDIA under the patents or other intellectual property rights of NVIDIA. This should be our first candidate function for parallelization. The dimension and size of blocks per grid and the dimension and size of threads per block are both important factors. CUDA shared memory writes incur unexplainable long latency, CUDA atomic function usage with volatile shared memory. On integrated GPUs (i.e., GPUs with the integrated field of the CUDA device properties structure set to 1), mapped pinned memory is always a performance gain because it avoids superfluous copies as integrated GPU and CPU memory are physically the same. \left( 0.877 \times 10^{9} \right. :class table-no-stripes, Table 3. See the CUDA C++ Programming Guide for further explanations and software requirements for UVA and P2P. The texture cache is optimized for 2D spatial locality, so threads of the same warp that read texture addresses that are close together will achieve best performance. Why do academics stay as adjuncts for years rather than move around? The NVIDIA Nsight Visual Studio Edition for Microsoft Windows 7, Windows HPC Server 2008, Windows 8.1, and Windows 10 is available as a free plugin for Microsoft Visual Studio; see: https://developer.nvidia.com/nsight-visual-studio-edition. Recommendations for taking advantage of minor version compatibility in your application, 16.4. Applications already using other BLAS libraries can often quite easily switch to cuBLAS, for example, whereas applications that do little to no linear algebra will have little use for cuBLAS. For purposes of calculating occupancy, the number of registers used by each thread is one of the key factors. The reads of elements in transposedTile within the for loop are free of conflicts, because threads of each half warp read across rows of the tile, resulting in unit stride across the banks. To minimize bank conflicts, it is important to understand how memory addresses map to memory banks and how to optimally schedule memory requests. If x is the coordinate and N is the number of texels for a one-dimensional texture, then with clamp, x is replaced by 0 if x < 0 and by 1-1/N if 1