High Priority: Avoid different execution paths within the same warp. To do so, use this equation: \(\text{Effective\ bandwidth} = \left( {\left( B_{r} + B_{w} \right) \div 10^{9}} \right) \div \text{time}\). Therefore, to accurately measure the elapsed time for a particular call or sequence of CUDA calls, it is necessary to synchronize the CPU thread with the GPU by calling cudaDeviceSynchronize() immediately before starting and stopping the CPU timer. CUDA calls and kernel executions can be timed using either CPU or GPU timers. Last updated on Feb 27, 2023. See the nvidia-smi documenation for details. Missing dependencies is also a binary compatibility break, hence you should provide fallbacks or guards for functionality that depends on those interfaces. Alternatively, NVRTC can generate cubins directly starting with CUDA 11.1. The details of managing the accelerator device are handled implicitly by an OpenACC-enabled compiler and runtime. These memory spaces include global, local, shared, texture, and registers, as shown in Figure 2. For example, many kernels have complex addressing logic for accessing memory in addition to their actual computation. Hence, the A100 GPU enables a single thread block to address up to 163 KB of shared memory and GPUs with compute capability 8.6 can address up to 99 KB of shared memory in a single thread block. As even CPU architectures will require exposing parallelism in order to improve or simply maintain the performance of sequential applications, the CUDA family of parallel programming languages (CUDA C++, CUDA Fortran, etc.) If multiple CUDA application processes access the same GPU concurrently, this almost always implies multiple contexts, since a context is tied to a particular host process unless Multi-Process Service is in use. Load the GPU program and execute, caching data on-chip for performance. This is done with the FLDCW x86 assembly instruction or the equivalent operating system API. Shared memory is a powerful feature for writing well optimized CUDA code. Consequently, the order in which arithmetic operations are performed is important. However, striding through global memory is problematic regardless of the generation of the CUDA hardware, and would seem to be unavoidable in many cases, such as when accessing elements in a multidimensional array along the second and higher dimensions. As a result, it is recommended that first-time readers proceed through the guide sequentially. Overall, best performance is achieved when using asynchronous copies with an element of size 8 or 16 bytes. For example, to use only devices 0 and 2 from the system-wide list of devices, set CUDA_VISIBLE_DEVICES=0,2 before launching the application. There's no way around this. For devices of compute capability 1.x, the warp size is 32 threads and the number of banks is 16. It provides functions to handle the following: Interoperability with OpenGL and Direct3D. Prior to UVA, an application had to keep track of which pointers referred to device memory (and for which device) and which referred to host memory as a separate bit of metadata (or as hard-coded information in the program) for each pointer. The for loop over i multiplies a row of A by a column of B, which is then written to C. The effective bandwidth of this kernel is 119.9 GB/s on an NVIDIA Tesla V100. All CUDA compute devices follow the IEEE 754 standard for binary floating-point representation, with some small exceptions. (See Data Transfer Between Host and Device.) Note that Gustafsons Law assumes that the ratio of serial to parallel execution remains constant, reflecting additional cost in setting up and handling the larger problem. A pointer to a structure with a size embedded is a better solution. Shared memory is specified by the device architecture and is measured on per-block basis. Devices of compute capability 8.6 have 2x more FP32 operations per cycle per SM than devices of compute capability 8.0. This capability (combined with thread synchronization) has a number of uses, such as user-managed data caches, high-performance cooperative parallel algorithms (parallel reductions, for example), and tofacilitate global memory coalescing in cases where it would otherwise not be possible. It also avoids an intermediary register file access traditionally present between the global memory read and the shared memory write. While the contents can be used as a reference manual, you should be aware that some topics are revisited in different contexts as various programming and configuration topics are explored. x86 processors can use an 80-bit double extended precision math when performing floating-point calculations. Is it possible to create a concave light? The first segment shows the reference sequential implementation, which transfers and operates on an array of N floats (where N is assumed to be evenly divisible by nThreads). In the case of texture access, if a texture reference is bound to a linear array in global memory, then the device code can write to the underlying array. Non-default streams (streams other than stream 0) are required for concurrent execution because 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. This typically involves arithmetic on large data sets (such as matrices) where the same operation can be performed across thousands, if not millions, of elements at the same time. Obtaining the right answer is clearly the principal goal of all computation. 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. Providing the two argument version of __launch_bounds__(maxThreadsPerBlock,minBlocksPerMultiprocessor) can improve performance in some cases. Code that transfers data for brief use by a small number of threads will see little or no performance benefit. 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. The NVIDIA A100 GPU supports shared memory capacity of 0, 8, 16, 32, 64, 100, 132 or 164 KB per SM. When using branch predication, none of the instructions whose execution depends on the controlling condition is skipped. On Windows, if the CUDA Runtime or other dynamically-linked CUDA Toolkit library is placed in the same directory as the executable, Windows will locate it automatically. Is it known that BQP is not contained within NP? CUDA Memory Rules Currently can only transfer data from host to global (and constant memory) and not host directly to shared. Users should refer to the CUDA headers and documentation for new CUDA APIs introduced in a release. To ensure correct results when parallel threads cooperate, we must synchronize the threads. The results of these optimizations are summarized in Table 3. This action leads to a load of eight L2 cache segments per warp on the Tesla V100 (compute capability 7.0). Copyright 2020-2023, NVIDIA Corporation & Affiliates. Sometimes, the compiler may unroll loops or optimize out if or switch statements by using branch predication instead. 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. When redistributing the dynamically-linked versions of one or more CUDA libraries, it is important to identify the exact files that need to be redistributed. 1 Answer Sorted by: 2 You don't need to worry about this. The performance of the above kernel is shown in the chart below. If textures are fetched using tex1D(),tex2D(), or tex3D() rather than tex1Dfetch(), the hardware provides other capabilities that might be useful for some applications such as image processing, as shown in Table 4. Checking these things frequently as an integral part of our cyclical APOD process will help ensure that we achieve the desired results as rapidly as possible. 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. Weak Scaling and Gustafsons Law, 3.1.3.3. For more details on the new warp wide reduction operations refer to Warp Reduce Functions in the CUDA C++ Programming Guide. exchange data) between threadblocks, the only method is to use global memory. NVIDIA makes no representation or warranty that products based on this document will be suitable for any specified use. 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. Comparing Performance of Synchronous vs Asynchronous Copy from Global Memory to Shared Memory. In the asynchronous version of the kernel, instructions to load from global memory and store directly into shared memory are issued as soon as __pipeline_memcpy_async() function is called. Prior to CUDA 11.0, the minimum driver version for a toolkit was the same as the driver shipped with that version of the CUDA Toolkit. Delays in rolling out new NVIDIA drivers could mean that users of such systems may not have access to new features available in CUDA releases. The right value for minBlocksPerMultiprocessor should be determined using a detailed per kernel analysis. The NVIDIA A100 GPU based on compute capability 8.0 increases the maximum capacity of the combined L1 cache, texture cache and shared memory to 192 KB, 50% larger than the L1 cache in NVIDIA V100 GPU. As a useful side effect, this strategy will allow us a means to reduce code duplication should we wish to include both CPU and GPU execution paths in our application: if the bulk of the work of our CUDA kernels is done in __host__ __device__ functions, we can easily call those functions from both the host code and the device code without duplication. Sharing data between blocks - CUDA Programming and Performance - NVIDIA Hardware Acceleration for Split Arrive/Wait Barrier, 1.4.1.4. (For further information, refer to Performance Guidelines in the CUDA C++ Programming Guide). Going a step further, if most functions are defined as __host__ __device__ rather than just __device__ functions, then these functions can be tested on both the CPU and the GPU, thereby increasing our confidence that the function is correct and that there will not be any unexpected differences in the results. We adjust the copy_count in the kernels such that each thread block copies from 512 bytes up to 48 MB. All threads within one block see the same shared memory array . The issue here is the number of operations performed per data element transferred. In fact, shared memory latency is roughly 100x lower than uncached global memory latency (provided that there are no bank conflicts between the threads, which we will examine later in this post). This information is obtained by calling cudaGetDeviceProperties() and accessing the information in the structure it returns. Likewise, for exponentation with an exponent of -1/3, use rcbrt() or rcbrtf(). This feature enables CUDA kernels to overlap copying data from global to shared memory with computation. See Building for Maximum Compatibility for further discussion of the flags used for building code for multiple generations of CUDA-capable device simultaneously. Before I show you how to avoid striding through global memory in the next post, first I need to describe shared memory in some detail. For example, transferring two matrices to the device to perform a matrix addition and then transferring the results back to the host will not realize much performance benefit. For some applications the problem size will remain constant and hence only strong scaling is applicable. CUDA Toolkit Library Redistribution, 16.4.1.2. How to notate a grace note at the start of a bar with lilypond? Customer should obtain the latest relevant information before placing orders and should verify that such information is current and complete. Shared memory is a powerful feature for writing well-optimized CUDA code. Memory optimizations are the most important area for performance. Whether a device has this capability is indicated by the concurrentKernels field of the cudaDeviceProp structure (or listed in the output of the deviceQuery CUDA Sample). The interface is augmented to retrieve either the PTX or cubin if an actual architecture is specified. The functions exp2(), exp2f(), exp10(), and exp10f(), on the other hand, are similar to exp() and expf() in terms of performance, and can be as much as ten times faster than their pow()/powf() equivalents. These barriers can also be used alongside the asynchronous copy. NVIDIA Ampere GPU Architecture Tuning Guide, 1.4. We define binary compatibility as a set of guarantees provided by the library, where an application targeting the said library will continue to work when dynamically linked against a different version of the library. Information published by NVIDIA regarding third-party products or services does not constitute a license from NVIDIA to use such products or services or a warranty or endorsement thereof. For other applications, the problem size will grow to fill the available processors. Because shared memory is shared by threads in a thread block, it provides a mechanism for threads to cooperate. In particular, developers should note the number of multiprocessors on the device, the number of registers and the amount of memory available, and any special capabilities of the device. For the NVIDIA Tesla V100, global memory accesses with no offset or with offsets that are multiples of 8 words result in four 32-byte transactions. The dimension and size of blocks per grid and the dimension and size of threads per block are both important factors. Finally, higher bandwidth between the host and the device is achieved when using page-locked (or pinned) memory, as discussed in the CUDA C++ Programming Guide and the Pinned Memory section of this document. A device in which work is poorly balanced across the multiprocessors will deliver suboptimal performance. This difference is illustrated in Figure 13. Loop Counters Signed vs. Unsigned, 11.1.5. The compiler and hardware thread scheduler will schedule instructions as optimally as possible to avoid register memory bank conflicts. 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. The following documents are especially important resources: In particular, the optimization section of this guide assumes that you have already successfully downloaded and installed the CUDA Toolkit (if not, please refer to the relevant CUDA Installation Guide for your platform) and that you have a basic familiarity with the CUDA C++ programming language and environment (if not, please refer to the CUDA C++ Programming Guide). This approach will greatly improve your understanding of effective programming practices and enable you to better use the guide for reference later. The most straightforward approach to parallelizing an application is to leverage existing libraries that take advantage of parallel architectures on our behalf. 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. This guide refers to and relies on several other documents that you should have at your disposal for reference, all of which are available at no cost from the CUDA website https://docs.nvidia.com/cuda/. Therefore, an application that compiled successfully on an older version of the toolkit may require changes in order to compile against a newer version of the toolkit. 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. CUDA driver - User-mode driver component used to run CUDA applications (e.g. Current utilization rates are reported for both the compute resources of the GPU and the memory interface. Hence, access to local memory is as expensive as access to global memory. See Math Libraries. Optimizations can be applied at various levels, from overlapping data transfers with computation all the way down to fine-tuning floating-point operation sequences. For those exponentiations where the exponent is not exactly representable as a floating-point number, such as 1/3, this can also provide much more accurate results, as use of pow() magnifies the initial representational error. Support for TF32 Tensor Core, through HMMA instructions. An optimized handling of strided accesses using coalesced reads from global memory uses the shared transposedTile to avoid uncoalesced accesses in the second term in the dot product and the shared aTile technique from the previous example to avoid uncoalesced accesses in the first term. By simply increasing this parameter (without modifying the kernel), it is possible to effectively reduce the occupancy of the kernel and measure its effect on performance. For most purposes, the key point is that the larger the parallelizable portion P is, the greater the potential speedup. In such cases, kernels with 32x32 or 64x16 threads can be launched with each thread processing four elements of the shared memory array. This advantage is increased when several powers of the same base are needed (e.g., where both x2 and x5 are calculated in close proximity), as this aids the compiler in its common sub-expression elimination (CSE) optimization. These accept one of three options:cudaFuncCachePreferNone, cudaFuncCachePreferShared, and cudaFuncCachePreferL1. Static linking makes the executable slightly larger, but it ensures that the correct version of runtime library functions are included in the application binary without requiring separate redistribution of the CUDA Runtime library.
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