Connect and share knowledge within a single location that is structured and easy to search. All kernel launches are asynchronous, as are memory-copy functions with the Async suffix on their names. As with the dynamically-linked version of the CUDA Runtime library, these libraries should be bundled with the application executable when distributing that application. 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. 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that compiled successfully on an older version of the toolkit may require changes in order to compile against a newer version of the toolkit. Maximizing parallel execution starts with structuring the algorithm in a way that exposes as much parallelism as possible. Overall, developers can expect similar occupancy as on Volta without changes to their application. Medium Priority: Prefer faster, more specialized math functions over slower, more general ones when possible. For devices of compute capability 6.0 or higher, the requirements can be summarized quite easily: the concurrent accesses of the threads of a warp will coalesce into a number of transactions equal to the number of 32-byte transactions necessary to service all of the threads of the warp. This is because the user could only allocate the CUDA static shared memory up to 48 KB. 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. To analyze performance, it is necessary to consider how warps access global memory in the for loop. Integer division and modulo operations are particularly costly and should be avoided or replaced with bitwise operations whenever possible: If \(n\) is a power of 2, ( \(i/n\) ) is equivalent to ( \(i \gg {log2}(n)\) ) and ( \(i\% n\) ) is equivalent to ( \(i\&\left( {n - 1} \right)\) ). This spreadsheet, shown in Figure 15, is called CUDA_Occupancy_Calculator.xls and is located in the tools subdirectory of the CUDA Toolkit installation. CUDA provides a simple barrier synchronization primitive, __syncthreads(). This technique could be used when the data dependency is such that the data can be broken into chunks and transferred in multiple stages, launching multiple kernels to operate on each chunk as it arrives. Staged concurrent copy and execute shows how the transfer and kernel execution can be broken up into nStreams stages. As seen above, in the case of misaligned sequential accesses, caches help to alleviate the performance impact. Staging Ground Beta 1 Recap, and Reviewers needed for Beta 2, Atomic operations on Shared Memory in CUDA. Sometimes, the compiler may unroll loops or optimize out if or switch statements by using branch predication instead. 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. It accepts CUDA C++ source code in character string form and creates handles that can be used to obtain the PTX. We will note some of them later on in the document. My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? Optimizations can be applied at various levels, from overlapping data transfers with computation all the way down to fine-tuning floating-point operation sequences. The CUDA Runtime handles kernel loading and setting up kernel parameters and launch configuration before the kernel is launched. Recovering from a blunder I made while emailing a professor. To achieve high memory bandwidth for concurrent accesses, shared memory is divided into equally sized memory modules (banks) that can be accessed simultaneously. You must declare a single extern unsized array as before, and use pointers into it to divide it into multiple arrays, as in the following excerpt. High Priority: To maximize developer productivity, profile the application to determine hotspots and bottlenecks. If the GPU must wait on one warp of threads, it simply begins executing work on another. Registers are allocated to an entire block all at once. 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. Notwithstanding any damages that customer might incur for any reason whatsoever, NVIDIAs aggregate and cumulative liability towards customer for the products described herein shall be limited in accordance with the Terms of Sale for the product. While compiler optimization improvements continually seek to narrow this gap, explicit multiplication (or the use of an equivalent purpose-built inline function or macro) can have a significant advantage. Indicate whether compute processes can run on the GPU and whether they run exclusively or concurrently with other compute processes. The cudaChooseDevice() function can be used to select the device that most closely matches a desired set of features. Furthermore, this file should be installed into the @rpath of the application; see Where to Install Redistributed CUDA Libraries. The key here is that libraries are most useful when they match well with the needs of the application. 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. While the details of how to apply these strategies to a particular application is a complex and problem-specific topic, the general themes listed here apply regardless of whether we are parallelizing code to run on for multicore CPUs or for use on CUDA GPUs. An exception is the case where all threads in a warp address the same shared memory address, resulting in a broadcast. Replacing broken pins/legs on a DIP IC package. 11.x). When JIT compilation of PTX device code is used, the NVIDIA driver caches the resulting binary code on disk. When choosing the first execution configuration parameter-the number of blocks per grid, or grid size - the primary concern is keeping the entire GPU busy. The performance guidelines and best practices described in the CUDA C++ Programming Guide and the CUDA C++ Best Practices Guide apply to all CUDA-capable GPU architectures. For example, on IBM Newell POWER9 nodes (where the CPUs correspond to NUMA nodes 0 and 8), use: One of the keys to good performance is to keep the multiprocessors on the device as busy as possible. Starting with CUDA 11.0, devices of compute capability 8.0 and above have the capability to influence persistence of data in the L2 cache. Parallelizing these functions as well should increase our speedup potential. Best performance with synchronous copy is achieved when the copy_count parameter is a multiple of 4 for all three element sizes. Although each of these instructions is scheduled for execution, only the instructions with a true predicate are actually executed. Low Medium Priority: Use signed integers rather than unsigned integers as loop counters. Fast, low-precision interpolation between texels, Valid only if the texture reference returns floating-point data, Can be used only with normalized texture coordinates, 1 The automatic handling of boundary cases in the bottom row of Table 4 refers to how a texture coordinate is resolved when it falls outside the valid addressing range. Using Kolmogorov complexity to measure difficulty of problems? Testing of all parameters of each product is not necessarily performed by NVIDIA. The PTX string generated by NVRTC can be loaded by cuModuleLoadData and cuModuleLoadDataEx. It can be simpler to view N as a very large number, which essentially transforms the equation into \(S = 1/(1 - P)\). The following example is based on gprof, which is an open-source profiler for Linux platforms from the GNU Binutils collection. An example would be modeling how two molecules interact with each other, where the molecule sizes are fixed. Therefore, it is best to avoid multiple contexts per GPU within the same CUDA application. There are multiple ways to declare shared memory inside a kernel, depending on whether the amount of memory is known at compile time or at run time. The compiler must on occasion insert conversion instructions, introducing additional execution cycles. The throughput of __sinf(x), __cosf(x), and__expf(x) is much greater than that of sinf(x), cosf(x), and expf(x). The NVIDIA Ampere GPU architecture adds hardware acceleration for copying data from global memory to shared memory. The L2 cache set-aside size for persisting accesses may be adjusted, within limits: Mapping of user data to L2 set-aside portion can be controlled using an access policy window on a CUDA stream or CUDA graph kernel node. On Linux systems, the CUDA driver and kernel mode components are delivered together in the NVIDIA display driver package. If A, B, and C are floating-point values, (A+B)+C is not guaranteed to equal A+(B+C) as it is in symbolic math. In Unoptimized handling of strided accesses to global memory, the row-th, col-th element of C is obtained by taking the dot product of the row-th and col-th rows of A. Latency hiding and occupancy depend on the number of active warps per multiprocessor, which is implicitly determined by the execution parameters along with resource (register and shared memory) constraints. To enable the loads from global memory to be coalesced, data are read from global memory sequentially. 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. In such a case, the bandwidth would be 836.4 GiB/s. 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. For the latter variety of application, some degree of code refactoring to expose the inherent parallelism in the application might be necessary, but keep in mind that this refactoring work will tend to benefit all future architectures, CPU and GPU alike, so it is well worth the effort should it become necessary. 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. CUDA Compatibility Developers Guide, 15.3.1. With the use of shared memory we can fetch data from global memory and place it into on-chip memory with far lower latency and higher bandwidth then global memory. Various dynamic and static information is reported, including board serial numbers, PCI device IDs, VBIOS/Inforom version numbers and product names. After the application is dynamically linked against the CUDA Runtime, this version of the runtime library should be bundled with the application. Several third-party debuggers support CUDA debugging as well; see: https://developer.nvidia.com/debugging-solutions for more details. CUDA-GDB is a port of the GNU Debugger that runs on Linux and Mac; see: https://developer.nvidia.com/cuda-gdb. Many software libraries and applications built on top of CUDA (e.g. Data copied from global memory to shared memory using asynchronous copy instructions can be cached in the L1 cache or the L1 cache can be optionally bypassed. 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 read-only texture memory space is cached. The host code in Zero-copy host code shows how zero copy is typically set up. The programming guide to using the CUDA Toolkit to obtain the best performance from NVIDIA GPUs. If the PTX is also not available, then the kernel launch will fail. In this guide, they represent a typical case. 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. // Type of access property on cache miss. Be aware that CPU-to-GPU synchronization points such as those mentioned in this section imply a stall in the GPUs processing pipeline and should thus be used sparingly to minimize their performance impact. 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. I think this pretty much implies that you are going to have the place the heads of each queue in global memory. Block-column matrix multiplied by block-row matrix. As mentioned in Occupancy, higher occupancy does not always equate to better performance. The third generation of NVIDIAs high-speed NVLink interconnect is implemented in A100 GPUs, which significantly enhances multi-GPU scalability, performance, and reliability with more links per GPU, much faster communication bandwidth, and improved error-detection and recovery features. The CUDA runtime has relaxed the minimum driver version check and thus no longer requires a driver upgrade when moving to a new minor release. Compatibility of the CUDA platform is thus intended to address a few scenarios: NVIDIA driver upgrades to systems with GPUs running in production for enterprises or datacenters can be complex and may need advance planning. If the transfer time exceeds the execution time, a rough estimate for the overall time is tT + tE/nStreams. This microbenchmark uses a 1024 MB region in GPU global memory. This does not apply to the NVIDIA Driver; the end user must still download and install an NVIDIA Driver appropriate to their GPU(s) and operating system. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Not the answer you're looking for? 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. These transfers are costly in terms of performance and should be minimized. To measure performance accurately, it is useful to calculate theoretical and effective bandwidth. However, bank conflicts occur when copying the tile from global memory into shared memory. 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]. Increased L2 capacity and L2 Residency Controls, 1.4.2.3. 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. In many applications, a combination of strong and weak scaling is desirable. The nature of simulating nature: A Q&A with IBM Quantum researcher Dr. Jamie We've added a "Necessary cookies only" option to the cookie consent popup. As a result, it is recommended that first-time readers proceed through the guide sequentially. Constantly recompiling with the latest CUDA Toolkit means forcing upgrades on the end-customers of an application product. A key concept in this effort is occupancy, which is explained in the following sections. Performance benefits can be more readily achieved when this ratio is higher. Please note that new versions of nvidia-smi are not guaranteed to be backward-compatible with previous versions. The example below shows how to use the access policy window on a CUDA stream. This situation is not different from what is available today where developers use macros to compile out features based on CUDA versions. 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. The results of the various optimizations are summarized in Table 2. 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. While NVIDIA GPUs are frequently associated with graphics, they are also powerful arithmetic engines capable of running thousands of lightweight threads in parallel. Indicate whether the NVIDIA driver stays loaded when no applications are connected to the GPU. The compiler replaces a branch instruction with predicated instructions only if the number of instructions controlled by the branch condition is less than or equal to a certain threshold. However, since APOD is a cyclical process, we might opt to parallelize these functions in a subsequent APOD pass, thereby limiting the scope of our work in any given pass to a smaller set of incremental changes. NVIDIA hereby expressly objects to applying any customer general terms and conditions with regards to the purchase of the NVIDIA product referenced in this document. Execution Configuration Optimizations, 11.1.2. GPUs with compute capability 8.6 support shared memory capacity of 0, 8, 16, 32, 64 or 100 KB per SM. This context can be current to as many threads as desired within the creating process, and cuDevicePrimaryCtxRetain will fail if a non-primary context that was created with the CUDA driver API already exists on the device. The number of copy engines on a GPU is given by the asyncEngineCount field of the cudaDeviceProp structure, which is also listed in the output of the deviceQuery CUDA Sample. These accept one of three options:cudaFuncCachePreferNone, cudaFuncCachePreferShared, and cudaFuncCachePreferL1. Actions that present substantial improvements for most CUDA applications have the highest priority, while small optimizations that affect only very specific situations are given a lower priority. This data will thus use the L2 set-aside portion. The NVIDIA Management Library (NVML) is a C-based interface that provides direct access to the queries and commands exposed via nvidia-smi intended as a platform for building 3rd-party system management applications. For example, the compiler may use predication to avoid an actual branch. On devices that are capable of concurrent kernel execution, streams can also be used to execute multiple kernels simultaneously to more fully take advantage of the devices multiprocessors. As illustrated in Figure 7, non-unit-stride global memory accesses should be avoided whenever possible. There is a total of 64 KB constant memory on a device. By understanding how applications can scale it is possible to set expectations and plan an incremental parallelization strategy. Not requiring driver updates for new CUDA releases can mean that new versions of the software can be made available faster to users. There are several key strategies for parallelizing sequential code. By using new CUDA versions, users can benefit from new CUDA programming model APIs, compiler optimizations and math library features. The interface is augmented to retrieve either the PTX or cubin if an actual architecture is specified. Shared memory enables cooperation between threads in a block. No license, either expressed or implied, is granted under any NVIDIA patent right, copyright, or other NVIDIA intellectual property right under this document. For portability, that is, to be able to execute code on future GPU architectures with higher compute capability (for which no binary code can be generated yet), an application must load PTX code that will be just-in-time compiled by the NVIDIA driver for these future devices. A CUDA context is a software environment that manages memory and other resources To do this, the simpleMultiply kernel (Unoptimized matrix multiplication) calculates the output elements of a tile of matrix C. In Unoptimized matrix multiplication, a, b, and c are pointers to global memory for the matrices A, B, and C, respectively; blockDim.x, blockDim.y, and TILE_DIM are all equal to w. Each thread in the wxw-thread block calculates one element in a tile of C. row and col are the row and column of the element in C being calculated by a particular thread. This approach is most straightforward when the majority of the total running time of our application is spent in a few relatively isolated portions of the code. A variant of the previous matrix multiplication can be used to illustrate how strided accesses to global memory, as well as shared memory bank conflicts, are handled. Devices of compute capability 2.0 and higher have the additional ability to multicast shared memory accesses, meaning that multiple accesses to the same location by any number of threads within a warp are served simultaneously. C++-style convenience wrappers (cuda_runtime.h) built on top of the C-style functions.