When an application will be deployed to target machines of arbitrary/unknown configuration, the application should explicitly test for the existence of a CUDA-capable GPU in order to take appropriate action when no such device is available. To execute code on devices of specific compute capability, an application must load binary or PTX code that is compatible with this compute capability. NVIDIA-SMI can be used to configure a GPU for exclusive process mode, which limits the number of contexts per GPU to one. The details of various CPU timing approaches are outside the scope of this document, but developers should always be aware of the resolution their timing calls provide. Constant memory used for data that does not change (i.e. This code reverses the data in a 64-element array using shared memory. Two types of runtime math operations are supported. In fact, local memory is off-chip. Since you don't indicate where your "locally sorted" data resides, this could indicate a copying of that much data at least (for example, if they are locally sorted and reside in shared memory). Context switches (when two threads are swapped) are therefore slow and expensive. In order to optimize the performance, when the size of the persistent data is more than the size of the set-aside L2 cache portion, we tune the num_bytes and hitRatio parameters in the access window as below. In the example above, we can clearly see that the function genTimeStep() takes one-third of the total running time of the application. Therefore, a texture fetch costs one device memory read only on a cache miss; otherwise, it just costs one read from the texture cache. Why do academics stay as adjuncts for years rather than move around? The maximum number of registers per thread is 255. A diagram depicting the timeline of execution for the two code segments is shown in Figure 1, and nStreams is equal to 4 for Staged concurrent copy and execute in the bottom half of the figure. It also disables single-precision denormal support and lowers the precision of single-precision division in general. Coalescing concepts are illustrated in the following simple examples. It supports a number of command-line parameters, of which the following are especially useful for optimization and related best practices: -maxrregcount=N specifies the maximum number of registers kernels can use at a per-file level. For example, on devices of compute capability 7.0 each multiprocessor has 65,536 32-bit registers and can have a maximum of 2048 simultaneous threads resident (64 warps x 32 threads per warp). These recommendations are categorized by priority, which is a blend of the effect of the recommendation and its scope. The NVIDIA A100 GPU supports shared memory capacity of 0, 8, 16, 32, 64, 100, 132 or 164 KB per SM. The read-only texture memory space is cached. The CUDA Toolkits End-User License Agreement (EULA) allows for redistribution of many of the CUDA libraries under certain terms and conditions. For best performance, there should be some coherence in memory access by adjacent threads running on the device. As the stride increases, the effective bandwidth decreases until the point where 32 32-byte segments are loaded for the 32 threads in a warp, as indicated in Figure 7. NVIDIA shall have no liability for the consequences or use of such information or for any infringement of patents or other rights of third parties that may result from its use. Any PTX device code loaded by an application at runtime is compiled further to binary code by the device driver. Verify that your library doesnt leak dependencies, breakages, namespaces, etc. When using the driver APIs directly, we recommend using the new driver entry point access API (cuGetProcAddress) documented here: CUDA Driver API :: CUDA Toolkit Documentation. Sometimes, the compiler may unroll loops or optimize out if or switch statements by using branch predication instead. To allocate an array in shared memory we . Kernels can be written using the CUDA instruction set architecture, called PTX, which is described in the PTX reference manual. The approach of using a single thread to process multiple elements of a shared memory array can be beneficial even if limits such as threads per block are not an issue. To understand the performance difference between synchronous copy and asynchronous copy of data from global memory to shared memory, consider the following micro benchmark CUDA kernels for demonstrating the synchronous and asynchronous approaches. Code that cannot be sufficiently parallelized should run on the host, unless doing so would result in excessive transfers between the host and the device. Medium Priority: Use the fast math library whenever speed trumps precision. Dont expose ABI structures that can change. A system with multiple GPUs may contain GPUs of different hardware versions and capabilities. This ensures your code is compatible. // Type of access property on cache miss. While a binary compiled for 8.0 will run as is on 8.6, it is recommended to compile explicitly for 8.6 to benefit from the increased FP32 throughput. read- only by GPU) Shared memory is said to provide up to 15x speed of global memory Registers have similar speed to shared memory if reading same address or no bank conicts. The NVIDIA Ampere GPU architecture retains and extends the same CUDA programming model provided by previous NVIDIA GPU architectures such as Turing and Volta, and applications that follow the best practices for those architectures should typically see speedups on the NVIDIA A100 GPU without any code changes. Page-locked or pinned memory transfers attain the highest bandwidth between the host and the device. Concurrent copy and execute illustrates the basic technique. In this particular example, the offset memory throughput achieved is, however, approximately 9/10th, because adjacent warps reuse the cache lines their neighbors fetched. This illustrates the use of the shared memory as a user-managed cache when the hardware L1 cache eviction policy does not match up well with the needs of the application or when L1 cache is not used for reads from global memory. cudaOccupancyMaxActiveBlocksPerMultiprocessor, to dynamically select launch configurations based on runtime parameters. Copyright 2007-2023, NVIDIA Corporation & Affiliates. Intermediate data structures should be created in device memory, operated on by the device, and destroyed without ever being mapped by the host or copied to host memory. To ensure correct results when parallel threads cooperate, we must synchronize the threads. In such cases, call cudaGetDeviceProperties() to determine whether the device is capable of a certain feature. Note that the process used for validating numerical results can easily be extended to validate performance results as well. "After the incident", I started to be more careful not to trip over things. On the other hand, some applications designs will require some amount of refactoring to expose their inherent parallelism. CUDA: Using shared memory between different kernels.. Clear single-bit and double-bit ECC error counts. NVIDIA accepts no liability for inclusion and/or use of NVIDIA products in such equipment or applications and therefore such inclusion and/or use is at customers own risk. The context is explicit in the CUDA Driver API but is entirely implicit in the CUDA Runtime API, which creates and manages contexts automatically. Performance benefits can be more readily achieved when this ratio is higher. Table 2. The performance of the kernels is shown in Figure 14. Applications composed with these differences in mind can treat the host and device together as a cohesive heterogeneous system wherein each processing unit is leveraged to do the kind of work it does best: sequential work on the host and parallel work on the device. Before addressing specific performance tuning issues covered in this guide, refer to the NVIDIA Ampere GPU Architecture Compatibility Guide for CUDA Applications to ensure that your application is compiled in a way that is compatible with the NVIDIA Ampere GPU Architecture. The discussions in this guide all use the C++ programming language, so you should be comfortable reading C++ code. UVA is also a necessary precondition for enabling peer-to-peer (P2P) transfer of data directly across the PCIe bus or NVLink for supported GPUs in supported configurations, bypassing host memory. The interface is augmented to retrieve either the PTX or cubin if an actual architecture is specified. Programmers must primarily focus on following those recommendations to achieve the best performance. The warp wide reduction operations support arithmetic add, min, and max operations on 32-bit signed and unsigned integers and bitwise and, or and xor operations on 32-bit unsigned integers. Understanding Scaling discusses the potential benefit we might expect from such parallelization. (It should be mentioned that it is not possible to overlap a blocking transfer with an asynchronous transfer, because the blocking transfer occurs in the default stream, so it will not begin until all previous CUDA calls complete. One or more compute capability versions can be specified to the nvcc compiler while building a file; compiling for the native compute capability for the target GPU(s) of the application is important to ensure that application kernels achieve the best possible performance and are able to use the features that are available on a given generation of GPU. But this technique is still useful for other access patterns, as Ill show in the next post.). This document is provided for information purposes only and shall not be regarded as a warranty of a certain functionality, condition, or quality of a product. The cudaGetDeviceProperties() function reports various features of the available devices, including the CUDA Compute Capability of the device (see also the Compute Capabilities section of the CUDA C++ Programming Guide). So, if each thread block uses many registers, the number of thread blocks that can be resident on a multiprocessor is reduced, thereby lowering the occupancy of the multiprocessor. To assist with this, the CUDA Driver API provides methods to access and manage a special context on each GPU called the primary context. NVIDIA Corporation (NVIDIA) makes no representations or warranties, expressed or implied, as to the accuracy or completeness of the information contained in this document and assumes no responsibility for any errors contained herein. In many cases, the amount of shared memory required by a kernel is related to the block size that was chosen, but the mapping of threads to shared memory elements does not need to be one-to-one. Improvement by reading additional data into shared memory. Recall that the initial assess step allowed the developer to determine an upper bound for the potential speedup attainable by accelerating given hotspots. It is customers sole responsibility to evaluate and determine the applicability of any information contained in this document, ensure the product is suitable and fit for the application planned by customer, and perform the necessary testing for the application in order to avoid a default of the application or the product. When using CPU timers, it is critical to remember that many CUDA API functions are asynchronous; that is, they return control back to the calling CPU thread prior to completing their work. A more robust approach is to selectively introduce calls to fast intrinsic functions only if merited by performance gains and where altered behavior can be tolerated. If a single block needs to load all queues, then all queues will need to be placed in global memory by their respective blocks. Useful Features for tex1D(), tex2D(), and tex3D() Fetches, __launch_bounds__(maxThreadsPerBlock,minBlocksPerMultiprocessor), Using the CUDA Occupancy Calculator to project GPU multiprocessor occupancy, cudaOccupancyMaxActiveBlocksPerMultiprocessor, // When the program/library launches work, // When the program/library is finished with the context, Table 5. This document is not a commitment to develop, release, or deliver any Material (defined below), code, or functionality. Assess, Parallelize, Optimize, Deploy, 3.1.3.1. Resources stay allocated to each thread until it completes its execution. High Priority: Avoid different execution paths within the same warp. The kernel is executed within a loop in host code that varies the parameter offset from 0 to 32. Figure 6 illustrates how threads in the CUDA device can access the different memory components. Devices of compute capability 1.0 to 1.3 have 16 KB/Block, compute 2.0 onwards have 48 KB/Block shared memory by default. In Using shared memory to improve the global memory load efficiency in matrix multiplication, each element in a tile of A is read from global memory only once, in a fully coalesced fashion (with no wasted bandwidth), to shared memory. For most purposes, the key point is that the larger the parallelizable portion P is, the greater the potential speedup. On the other hand, if the data is only accessed once, such data accesses can be considered to be streaming. Overlapping computation and data transfers. This recommendation is subject to resource availability; therefore, it should be determined in the context of the second execution parameter - the number of threads per block, or block size - as well as shared memory usage. Because separate registers are allocated to all active threads, no swapping of registers or other state need occur when switching among GPU threads. Low Priority: Use zero-copy operations on integrated GPUs for CUDA Toolkit version 2.2 and later. Code that uses the warp shuffle operation, for example, must be compiled with -arch=sm_30 (or higher compute capability). Conversely, if P is a small number (meaning that the application is not substantially parallelizable), increasing the number of processors N does little to improve performance. However we now add the underlying driver to that mix. For exponentiation using base 2 or 10, use the functions exp2() or expf2() and exp10() or expf10() rather than the functions pow() or powf(). Register dependencies arise when an instruction uses a result stored in a register written by an instruction before it. As a result, a read from constant memory costs one memory read from device memory only on a cache miss; otherwise, it just costs one read from the constant cache. Medium Priority: The number of threads per block should be a multiple of 32 threads, because this provides optimal computing efficiency and facilitates coalescing. On Linux and Mac, the -rpath linker option should be used to instruct the executable to search its local path for these libraries before searching the system paths: It may be necessary to adjust the value of -ccbin to reflect the location of your Visual Studio installation. - the incident has nothing to do with me; can I use this this way? CUDA: Shared memory allocation with overlapping borders On Systems on a Chip with integrated GPUs, such as NVIDIA Tegra, host and device memory are physically the same, but there is still a logical distinction between host and device memory. See the Application Note on CUDA for Tegra for details. NVIDIA accepts no liability related to any default, damage, costs, or problem which may be based on or attributable to: (i) the use of the NVIDIA product in any manner that is contrary to this document or (ii) customer product designs. Because transfers should be minimized, programs that run multiple kernels on the same data should favor leaving the data on the device between kernel calls, rather than transferring intermediate results to the host and then sending them back to the device for subsequent calculations. Code that transfers data for brief use by a small number of threads will see little or no performance benefit. Because the memory copy and the kernel both return control to the host immediately, the host function cpuFunction() overlaps their execution. 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. Using asynchronous copies does not use any intermediate register. Hence, access to local memory is as expensive as access to global memory. Since there are many possible optimizations that can be considered, having a good understanding of the needs of the application can help to make the process as smooth as possible. Both of your questions imply some sort of global synchronization. This approach will tend to provide the best results for the time invested and will avoid the trap of premature optimization. The difference between the phonemes /p/ and /b/ in Japanese. To target specific versions of NVIDIA hardware and CUDA software, use the -arch, -code, and -gencode options of nvcc. This limitation is not specific to CUDA, but an inherent part of parallel computation on floating-point values. Threads copy the data from global memory to shared memory with the statement s[t] = d[t], and the reversal is done two lines later with the statement d[t] = s[tr]. NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any time without notice. Using the CUDA Occupancy Calculator to project GPU multiprocessor occupancy. Shared memory is a CUDA memory space that is shared by all threads in a thread block. The effective bandwidth can vary by an order of magnitude depending on the access pattern for each type of memory. In this case shared means that all threads in a thread block can write and read to block-allocated shared memory, and all changes to this memory will be eventually available to all threads in the block. GPUs with compute capability 8.6 support shared memory capacity of 0, 8, 16, 32, 64 or 100 KB per SM. Likewise, for exponentation with an exponent of -1/3, use rcbrt() or rcbrtf(). Each warp of threads calculates one row of a tile of C, which depends on a single row of A and an entire tile of B as illustrated in Figure 12. nvidia-smi ships with NVIDIA GPU display drivers on Linux, and with 64-bit Windows Server 2008 R2 and Windows 7. nvidia-smi can output queried information as XML or as human-readable plain text either to standard output or to a file. An application has no direct control over these bank conflicts. On devices that are capable of concurrent copy and compute, it is possible to overlap kernel execution on the device with data transfers between the host and the device. . Finally, this product is divided by 109 to convert the result to GB/s. Throughout this guide, specific recommendations are made regarding the design and implementation of CUDA C++ code. Warp level support for Reduction Operations, 1.4.2.1. NVIDIA Ampere GPU Architecture Tuning Guide, 1.4. What sort of strategies would a medieval military use against a fantasy giant? Alternatively, the nvcc command-line option -arch=sm_XX can be used as a shorthand equivalent to the following more explicit -gencode= command-line options described above: However, while the -arch=sm_XX command-line option does result in inclusion of a PTX back-end target by default (due to the code=compute_XX target it implies), it can only specify a single target cubin architecture at a time, and it is not possible to use multiple -arch= options on the same nvcc command line, which is why the examples above use -gencode= explicitly. Results obtained using double-precision arithmetic will frequently differ from the same operation performed via single-precision arithmetic due to the greater precision of the former and due to rounding issues. Registers are allocated to an entire block all at once. In such cases, users or developers can still benefit from not having to upgrade the entire CUDA Toolkit or driver to use these libraries or frameworks. A copy kernel that illustrates misaligned accesses. This means that in one of these devices, for a multiprocessor to have 100% occupancy, each thread can use at most 32 registers. The same goes for other CUDA Toolkit libraries: cuFFT has an interface similar to that of FFTW, etc.
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