GPUs with a single copy engine can perform one asynchronous data transfer and execute kernels whereas GPUs with two copy engines can simultaneously perform one asynchronous data transfer from the host to the device, one asynchronous data transfer from the device to the host, and execute kernels. The functions that make up the CUDA Runtime API are explained in the CUDA Toolkit Reference Manual. High Priority: Avoid different execution paths within the same warp. However, this latency can be completely hidden by the execution of threads in other warps. Constant memory used for data that does not change (i.e. By using new CUDA versions, users can benefit from new CUDA programming model APIs, compiler optimizations and math library features. When our CUDA 11.1 application (i.e. Code that transfers data for brief use by a small number of threads will see little or no performance benefit. The dimension and size of blocks per grid and the dimension and size of threads per block are both important factors. For global memory accesses, this comparison of requested memory bandwidth to actual memory bandwidth is reported by the Global Memory Load Efficiency and Global Memory Store Efficiency metrics. The effective bandwidth for this kernel is 12.8 GB/s on an NVIDIA Tesla V100. For this reason, ensuring that as much as possible of the data in each cache line fetched is actually used is an important part of performance optimization of memory accesses on these devices. While processors are evolving to expose more fine-grained parallelism to the programmer, many existing applications have evolved either as serial codes or as coarse-grained parallel codes (for example, where the data is decomposed into regions processed in parallel, with sub-regions shared using MPI). The amount of performance benefit an application will realize by running on CUDA depends entirely on the extent to which it can be parallelized. No license, either expressed or implied, is granted under any NVIDIA patent right, copyright, or other NVIDIA intellectual property right under this document. If there are differences, then those differences will be seen early and can be understood in the context of a simple function. Refer to the CUDA Toolkit Release Notes for details for the minimum driver version and the version of the driver shipped with the toolkit. The current board power draw and power limits are reported for products that report these measurements. This should be our first candidate function for parallelization. NVIDIA GPU device driver - Kernel-mode driver component for NVIDIA GPUs. Mutually exclusive execution using std::atomic? 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). For example, the asyncEngineCount field of the device property structure indicates whether overlapping kernel execution and data transfers is possible (and, if so, how many concurrent transfers are possible); likewise, the canMapHostMemory field indicates whether zero-copy data transfers can be performed. In contrast with cudaMemcpy(), the asynchronous transfer version requires pinned host memory (see Pinned Memory), and it contains an additional argument, a stream ID. When using branch predication, none of the instructions whose execution depends on the controlling condition is skipped. Note that the timings are measured on the GPU clock, so the timing resolution is operating-system-independent. For a warp of threads, col represents sequential columns of the transpose of A, and therefore col*TILE_DIM represents a strided access of global memory with a stride of w, resulting in plenty of wasted bandwidth. As an example, the assignment operator in the following sample code has a high throughput, but, crucially, there is a latency of hundreds of clock cycles to read data from global memory: Much of this global memory latency can be hidden by the thread scheduler if there are sufficient independent arithmetic instructions that can be issued while waiting for the global memory access to complete. Page-locked memory mapping is enabled by calling cudaSetDeviceFlags() with cudaDeviceMapHost. However, a few rules of thumb should be followed: Threads per block should be a multiple of warp size to avoid wasting computation on under-populated warps and to facilitate coalescing. Setting the bank size to eight bytes can help avoid shared memory bank conflicts when accessing double precision data. I'm not sure if this will fit your overall processing. Pinned memory is allocated using the cudaHostAlloc() functions in the Runtime API. Declare shared memory in CUDA C/C++ device code using the__shared__variable declaration specifier. 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. Using shared memory to coalesce global reads. For example, on a device of compute capability 7.0, a kernel with 128-thread blocks using 37 registers per thread results in an occupancy of 75% with 12 active 128-thread blocks per multi-processor, whereas a kernel with 320-thread blocks using the same 37 registers per thread results in an occupancy of 63% because only four 320-thread blocks can reside on a multiprocessor. Scattered accesses increase ECC memory transfer overhead, especially when writing data to global memory. Support for Bfloat16 Tensor Core, through HMMA instructions. Asynchronous Copy from Global Memory to Shared Memory CUDA 11.0 introduces an async-copy feature that can be used within device code . See Registers for details. 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. CUDA Binary (cubin) Compatibility, 15.4. The implicit driver version checking, code initialization, CUDA context management, CUDA module management (cubin to function mapping), kernel configuration, and parameter passing are all performed by the CUDA Runtime. Starting with CUDA 11, the toolkit versions are based on an industry-standard semantic versioning scheme: .X.Y.Z, where: .X stands for the major version - APIs have changed and binary compatibility is broken. In fact, local memory is off-chip. In the kernel launch, specify the total shared memory needed, as in the following. For example, to compute the effective bandwidth of a 2048 x 2048 matrix copy, the following formula could be used: \(\text{Effective\ bandwidth} = \left( {\left( 2048^{2} \times 4 \times 2 \right) \div 10^{9}} \right) \div \text{time}\). Execution Configuration Optimizations, 11.1.2. By reversing the array using shared memory we are able to have all global memory reads and writes performed with unit stride, achieving full coalescing on any CUDA GPU. Note this switch is effective only on single-precision floating point. What if you need multiple dynamically sized arrays in a single kernel? Overall, developers can expect similar occupancy as on Volta without changes to their application. Maximizing parallel execution starts with structuring the algorithm in a way that exposes as much parallelism as possible. The CUDA driver ensures backward Binary Compatibility is maintained for compiled CUDA applications. The issue here is the number of operations performed per data element transferred. Use several smaller thread blocks rather than one large thread block per multiprocessor if latency affects performance. Shared memory can also be used to avoid uncoalesced memory accesses by loading and storing data in a coalesced pattern from global memory and then reordering it in shared memory. This chapter examines issues that can affect the correctness of returned data and points to appropriate solutions. These results are substantially lower than the corresponding measurements for the C = AB kernel. The following throughput metrics can be displayed in the Details or Detail Graphs view: The Requested Global Load Throughput and Requested Global Store Throughput values indicate the global memory throughput requested by the kernel and therefore correspond to the effective bandwidth obtained by the calculation shown under Effective Bandwidth Calculation. A stride of 2 results in a 50% of load/store efficiency since half the elements in the transaction are not used and represent wasted bandwidth. 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. Your code might reflect different priority factors. All CUDA compute devices follow the IEEE 754 standard for binary floating-point representation, with some small exceptions. Single-precision floats provide the best performance, and their use is highly encouraged. Both the CUDA driver and the CUDA runtime are not source compatible across the different SDK releases. Certain functionality might not be available so you should query where applicable. If a single block needs to load all queues, then all queues will need to be placed in global memory by their respective blocks. If an appropriate native binary (cubin) is not available, but the intermediate PTX code (which targets an abstract virtual instruction set and is used for forward-compatibility) is available, then the kernel will be compiled Just In Time (JIT) (see Compiler JIT Cache Management Tools) from the PTX to the native cubin for the device. It is however usually more effective to use a high-level programming language such as C++. and one element in the streaming data section. Note that the performance improvement is not due to improved coalescing in either case, but to avoiding redundant transfers from global memory. Many software libraries and applications built on top of CUDA (e.g. After the application is dynamically linked against the CUDA Runtime, this version of the runtime library should be bundled with the application. It is easy and informative to explore the ramifications of misaligned accesses using a simple copy kernel, such as the one in A copy kernel that illustrates misaligned accesses. Therefore, choosing sensible thread block sizes, such as multiples of the warp size (i.e., 32 on current GPUs), facilitates memory accesses by warps that are properly aligned. It is also the only way for applications to run on devices that did not exist at the time the application was compiled. 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. The NVIDIA Ampere GPU architecture includes new Third Generation Tensor Cores that are more powerful than the Tensor Cores used in Volta and Turing SMs. 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. See the nvidia-smi documenation for details. Performance Improvements Optimizing C = AB Matrix Multiply When attempting to optimize CUDA code, it pays to know how to measure performance accurately and to understand the role that bandwidth plays in performance measurement. 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. 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. In this scenario, CUDA initialization returns an error due to the minimum driver requirement. The latter become even more expensive (about an order of magnitude slower) if the magnitude of the argument x needs to be reduced. For example, the ability to overlap kernel execution with asynchronous data transfers between the host and the device is available on most but not all GPUs irrespective of the compute capability. 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. Therefore, to get the largest speedup for a fixed problem size, it is worthwhile to spend effort on increasing P, maximizing the amount of code that can be parallelized. Just-in-time compilation increases application load time but allows applications to benefit from latest compiler improvements. The right value for minBlocksPerMultiprocessor should be determined using a detailed per kernel analysis. This information is obtained by calling cudaGetDeviceProperties() and accessing the information in the structure it returns. Instead, strategies can be applied incrementally as they are learned. Having identified the hotspots and having done the basic exercises to set goals and expectations, the developer needs to parallelize the code. Using unrealistic workloads can lead to sub-optimal results and wasted effort both by causing developers to optimize for unrealistic problem sizes and by causing developers to concentrate on the wrong functions. This makes the code run faster at the cost of diminished precision and accuracy.
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