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. More information on cubins, PTX and application compatibility can be found in the CUDA C++ Programming Guide. Computing a row of a tile. HBM2 memories, on the other hand, provide dedicated ECC resources, allowing overhead-free ECC protection.2. Shared Memory. One method for doing so utilizes shared memory, which is discussed in the next section. To learn more, see our tips on writing great answers. 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. 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. 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. 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. 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. Unified Shared Memory/L1/Texture Cache, NVIDIA Ampere GPU Architecture Compatibility Guide for CUDA Applications. This means that in one of these devices, for a multiprocessor to have 100% occupancy, each thread can use at most 32 registers. Increment major versions when there are ABI breaking changes such as API deprecation and modifications. Having completed the GPU acceleration of one or more components of the application it is possible to compare the outcome with the original expectation. The results are shown in the chart below, where we see good performance regardless of whether the persistent data fits in the L2 set-aside or not. 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 CUDA runtime has relaxed the minimum driver version check and thus no longer requires a driver upgrade when moving to a new minor release. In certain addressing situations, reading device memory through texture fetching can be an advantageous alternative to reading device memory from global or constant memory. Fetching ECC bits for each memory transaction also reduced the effective bandwidth by approximately 20% compared to the same GPU with ECC disabled, though the exact impact of ECC on bandwidth can be higher and depends on the memory access pattern. The current board power draw and power limits are reported for products that report these measurements. If B has not finished writing its element before A tries to read it, we have a race condition, which can lead to undefined behavior and incorrect results. To scale to future devices, the number of blocks per kernel launch should be in the thousands. If this happens, the different execution paths must be executed separately; this increases the total number of instructions executed for this warp. Its result will often differ slightly from results obtained by doing the two operations separately. Bfloat16 provides 8-bit exponent i.e., same range as FP32, 7-bit mantissa and 1 sign-bit. 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. Note that in Improvement by reading additional data into shared memory, a __syncthreads() call is required after reading the B tile because a warp reads data from shared memory that were written to shared memory by different warps. Otherwise, five 32-byte segments are loaded per warp, and we would expect approximately 4/5th of the memory throughput achieved with no offsets. There are several key strategies for parallelizing sequential code. Pinned memory should not be overused. Also, because of the overhead associated with each transfer, batching many small transfers into one larger transfer performs significantly better than making each transfer separately, even if doing so requires packing non-contiguous regions of memory into a contiguous buffer and then unpacking after the transfer. Because the default stream, stream 0, exhibits serializing behavior for work on the device (an operation in the default stream can begin only after all preceding calls in any stream have completed; and no subsequent operation in any stream can begin until it finishes), these functions can be used reliably for timing in the default stream. If instead i is declared as signed, where the overflow semantics are undefined, the compiler has more leeway to use these optimizations. Many codes accomplish a significant portion of the work with a relatively small amount of code. Functions following functionName() naming convention are slower but have higher accuracy (e.g., sinf(x) and expf(x)). Therefore, it is best to avoid multiple contexts per GPU within the same CUDA application. (Factorization). The combined L1 cache capacity for GPUs with compute capability 8.6 is 128 KB. CUDA calls and kernel executions can be timed using either CPU or GPU timers. The kernel also uses the default stream, and it will not begin execution until the memory copy completes; therefore, no explicit synchronization is needed. One of several factors that determine occupancy is register availability. To enable the loads from global memory to be coalesced, data are read from global memory sequentially. When the persistent data region fits well into the 30 MB set-aside portion of the L2 cache, a performance increase of as much as 50% is observed. This is a requirement for good performance on CUDA: the software must use a large number (generally thousands or tens of thousands) of concurrent threads. Why do academics stay as adjuncts for years rather than move around? 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. As compared to the lower-level CUDA Driver API, the CUDA Runtime greatly eases device management by providing implicit initialization, context management, and device code module management. By understanding the end-users requirements and constraints and by applying Amdahls and Gustafsons laws, the developer can determine the upper bound of performance improvement from acceleration of the identified portions of the application. However, it also can act as a constraint on occupancy. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. NVIDIA products are sold subject to the NVIDIA standard terms and conditions of sale supplied at the time of order acknowledgement, unless otherwise agreed in an individual sales agreement signed by authorized representatives of NVIDIA and customer (Terms of Sale). Devices of compute capability 2.0 and later support a special addressing mode called Unified Virtual Addressing (UVA) on 64-bit Linux and Windows. Can airtags be tracked from an iMac desktop, with no iPhone? Alternatively, NVRTC can generate cubins directly starting with CUDA 11.1. However, this latency can be completely hidden by the execution of threads in other warps. The formulas in the table below are valid for x >= 0, x != -0, that is, signbit(x) == 0. Register pressure occurs when there are not enough registers available for a given task. If cudaGetDeviceCount() reports an error, the application should fall back to an alternative code path. Pinned memory is allocated using the cudaHostAlloc() functions in the Runtime API. Choosing the execution configuration parameters should be done in tandem; however, there are certain heuristics that apply to each parameter individually. 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. 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. Another important concept is the management of system resources allocated for a particular task. When an application depends on the availability of certain hardware or software capabilities to enable certain functionality, the CUDA API can be queried for details about the configuration of the available device and for the installed software versions. Furthermore, the need for context switching can reduce utilization when work from several contexts could otherwise execute concurrently (see also Concurrent Kernel Execution). 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. This ensures your code is compatible. NVIDIA GPU device driver - Kernel-mode driver component for NVIDIA GPUs. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Alternatively, NVIDIA provides an occupancy calculator in the form of an Excel spreadsheet that enables developers to hone in on the optimal balance and to test different possible scenarios more easily. Effective bandwidth is calculated by timing specific program activities and by knowing how data is accessed by the program. The cause of the difference is shared memory bank conflicts. Avoid long sequences of diverged execution by threads within the same warp. On all CUDA-enabled devices, it is possible to overlap host computation with asynchronous data transfers and with device computations. 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. Shared memory is a CUDA memory space that is shared by all threads in a thread block. However, bank conflicts occur when copying the tile from global memory into shared memory. NVIDIA products are not designed, authorized, or warranted to be suitable for use in medical, military, aircraft, space, or life support equipment, nor in applications where failure or malfunction of the NVIDIA product can reasonably be expected to result in personal injury, death, or property or environmental damage. Applications compiled against a CUDA Toolkit version will only run on systems with the specified minimum driver version for that toolkit version. A useful counterpart to the reference comparisons described above is to structure the code itself in such a way that is readily verifiable at the unit level. Dont expose ABI structures that can change. Detecting Hardware and Software Configuration. 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. Formulae for exponentiation by small fractions, Sample CUDA configuration data reported by deviceQuery, +-----------------------------------------------------------------------------+, |-------------------------------+----------------------+----------------------+, |===============================+======================+======================|, +-------------------------------+----------------------+----------------------+, |=============================================================================|, cudaDevAttrCanUseHostPointerForRegisteredMem, 1.3. 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). In the NVIDIA Ampere GPU architecture remote NVLINK accesses go through a Link TLB on the remote GPU. Because it is on-chip, shared memory has much higher bandwidth and lower latency than local and global memory - provided there are no bank conflicts between the threads, as detailed in the following section. Shared memory can be helpful in several situations, such as helping to coalesce or eliminate redundant access to global memory. 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 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. The OpenACC standard provides a set of compiler directives to specify loops and regions of code in standard C, C++ and Fortran that should be offloaded from a host CPU to an attached accelerator such as a CUDA GPU. What if you need multiple dynamically sized arrays in a single kernel? Data that cannot be laid out so as to enable coalescing, or that doesnt have enough locality to use the L1 or texture caches effectively, will tend to see lesser speedups when used in computations on GPUs. It is also the only way for applications to run on devices that did not exist at the time the application was compiled. 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. Applications with remote random accesses may want to constrain the remotely accessed region to 64 GB for each peer GPU. 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. Other peculiarities of floating-point arithmetic are presented in Features and Technical Specifications of the CUDA C++ Programming Guide as well as in a whitepaper and accompanying webinar on floating-point precision and performance available from https://developer.nvidia.com/content/precision-performance-floating-point-and-ieee-754-compliance-nvidia-gpus. Using shared memory to coalesce global reads. Data transfers between the host and the device using cudaMemcpy() are blocking transfers; that is, control is returned to the host thread only after the data transfer is complete. 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. When sharing data between threads, we need to be careful to avoid race conditions, because while threads in a block run logically in parallel, not all threads can execute physically at the same time. Certain functionality might not be available so you should query where applicable. The compiler must on occasion insert conversion instructions, introducing additional execution cycles. Both correctable single-bit and detectable double-bit errors are reported. The NVIDIA nvcc compiler driver converts .cu files into C++ for the host system and CUDA assembly or binary instructions for the device. How do I align things in the following tabular environment? Local memory is so named because its scope is local to the thread, not because of its physical location. 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. Weak scaling is a measure of how the time to solution changes as more processors are added to a system with a fixed problem size per processor; i.e., where the overall problem size increases as the number of processors is increased. For example, in the standard CUDA Toolkit installation, the files libcublas.so and libcublas.so.5.5 are both symlinks pointing to a specific build of cuBLAS, which is named like libcublas.so.5.5.x, where x is the build number (e.g., libcublas.so.5.5.17). The CUDA Runtime API provides developers with high-level C++ interface for simplified management of devices, kernel executions etc., While the CUDA driver API provides (CUDA Driver API) a low-level programming interface for applications to target NVIDIA hardware. This is done with the FLDCW x86 assembly instruction or the equivalent operating system API. rev2023.3.3.43278. Support for TF32 Tensor Core, through HMMA instructions. Instead, strategies can be applied incrementally as they are learned. The results of these optimizations are summarized in Table 3. For each iteration i of the for loop, the threads in a warp read a row of the B tile, which is a sequential and coalesced access for all compute capabilities. Copyright 2020-2023, NVIDIA Corporation & Affiliates. As a result, it is recommended that first-time readers proceed through the guide sequentially. Medium Priority: Use the fast math library whenever speed trumps precision. The results of the various optimizations are summarized in Table 2. This can be used to manage data caches, speed up high-performance cooperative parallel algorithms, and facilitate global memory coalescing in cases where it would otherwise not be possible. Applications compiled with CUDA toolkit versions as old as 3.2 will run on newer drivers. Now that we are working block by block, we should use shared memory. It will not allow any other CUDA call to begin until it has completed.) If you preorder a special airline meal (e.g. 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. The dynamic shared memory kernel, dynamicReverse(), declares the shared memory array using an unsized extern array syntax, extern shared int s[] (note the empty brackets and use of the extern specifier). Because of these nuances in register allocation and the fact that a multiprocessors shared memory is also partitioned between resident thread blocks, the exact relationship between register usage and occupancy can be difficult to determine. For recent versions of CUDA hardware, misaligned data accesses are not a big issue. 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. This is an aggressive optimization that can both reduce numerical accuracy and alter special case handling. High Priority: To maximize developer productivity, profile the application to determine hotspots and bottlenecks. For slightly better performance, however, they should instead be declared as signed. The same goes for other CUDA Toolkit libraries: cuFFT has an interface similar to that of FFTW, etc. See the CUDA C++ Programming Guide for further explanations and software requirements for UVA and P2P. We can then launch this kernel onto the GPU and retrieve the results without requiring major rewrites to the rest of our application. 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. The SONAME of the library against which the application was built must match the filename of the library that is redistributed with the application. With the CUDA Driver API, a CUDA application process can potentially create more than one context for a given GPU. For 32-bit applications, the file would be cublas32_55.dll. These results should be compared with those in Table 2. Understanding which type of scaling is most applicable to an application is an important part of estimating speedup. In such a case, the bandwidth would be 836.4 GiB/s. 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. The primary differences are in threading model and in separate physical memories: Execution pipelines on host systems can support a limited number of concurrent threads. It also avoids an intermediary register file access traditionally present between the global memory read and the shared memory write. Code that transfers data for brief use by a small number of threads will see little or no performance benefit. NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any time without notice. 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. NVLink operates transparently within the existing CUDA model. To specify an alternate path where the libraries will be distributed, use linker options similar to those below: For Linux and Mac, the -rpath option is used as before. When statically linking to the CUDA Runtime, multiple versions of the runtime can peacably coexist in the same application process simultaneously; for example, if an application uses one version of the CUDA Runtime, and a plugin to that application is statically linked to a different version, that is perfectly acceptable, as long as the installed NVIDIA Driver is sufficient for both. It presents established parallelization and optimization techniques and explains coding metaphors and idioms that can greatly simplify programming for CUDA-capable GPU architectures. Mapping Persistent data accesses to set-aside L2 in sliding window experiment. While multiple contexts (and their associated resources such as global memory allocations) can be allocated concurrently on a given GPU, only one of these contexts can execute work at any given moment on that GPU; contexts sharing the same GPU are time-sliced. As with the previous section on library building recommendations, if using the CUDA runtime, we recommend linking to the CUDA runtime statically when building your application. In such cases, call cudaGetDeviceProperties() to determine whether the device is capable of a certain feature. When we can, we should use registers. 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. Having a semantically versioned ABI means the interfaces need to be maintained and versioned. 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. However, if multiple threads requested addresses map to the same memory bank, the accesses are serialized. 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. Consequently, its important to understand the characteristics of the architecture. Register dependencies arise when an instruction uses a result stored in a register written by an instruction before it. 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. The effective bandwidth of this routine is 195.5 GB/s on an NVIDIA Tesla V100. A very important performance consideration in programming for CUDA-capable GPU architectures is the coalescing of global memory accesses. 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. The performance of the kernels is shown in Figure 14. Devices of compute capability 3.x allow a third setting of 32KB shared memory / 32KB L1 cache which can be obtained using the optioncudaFuncCachePreferEqual. Both pow() and powf() are heavy-weight functions in terms of register pressure and instruction count due to the numerous special cases arising in general exponentiation and the difficulty of achieving good accuracy across the entire ranges of the base and the exponent. (Developers targeting a single machine with known configuration may choose to skip this section.). Instructions with a false predicate do not write results, and they also do not evaluate addresses or read operands. For optimal performance, users should manually tune the NUMA characteristics of their application. Medium Priority: Prefer faster, more specialized math functions over slower, more general ones when possible. - the incident has nothing to do with me; can I use this this way? 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. The compiler can optimize groups of 4 load and store instructions. To execute code on devices of specific compute capability, an application must load binary or PTX code that is compatible with this compute capability. Best practices suggest that this optimization be performed after all higher-level optimizations have been completed. The following table presents the evolution of matrix instruction sizes and supported data types for Tensor Cores across different GPU architecture generations. 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. To illustrate the effect of strided access on effective bandwidth, see the kernel strideCopy() in A kernel to illustrate non-unit stride data copy, which copies data with a stride of stride elements between threads from idata to odata. Thedriver will honor the specified preference except when a kernel requires more shared memory per thread block than available in the specified configuration. The difference is in how threads in a half warp access elements of A in the second term, a[col*TILE_DIM+i], for each iteration i.