cuda shared memory between blocks cuda shared memory between blocks

Since some CUDA API calls and all kernel launches are asynchronous with respect to the host code, errors may be reported to the host asynchronously as well; often this occurs the next time the host and device synchronize with each other, such as during a call to cudaMemcpy() or to cudaDeviceSynchronize(). The operating system must swap threads on and off CPU execution channels to provide multithreading capability. Adjust kernel launch configuration to maximize device utilization. HBM2 memories, on the other hand, provide dedicated ECC resources, allowing overhead-free ECC protection.2. These instructions also avoid using extra registers for memory copies and can also bypass the L1 cache. When an application is built for multiple compute capabilities simultaneously (using several instances of the -gencode flag to nvcc), the binaries for the specified compute capabilities are combined into the executable, and the CUDA Driver selects the most appropriate binary at runtime according to the compute capability of the present device. This approach will tend to provide the best results for the time invested and will avoid the trap of premature optimization. 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. It is important to include the overhead of transferring data to and from the device in determining whether operations should be performed on the host or on the device. Support for Bfloat16 Tensor Core, through HMMA instructions. The use of shared memory is illustrated via the simple example of a matrix multiplication C = AB for the case with A of dimension Mxw, B of dimension wxN, and C of dimension MxN. This document is not a commitment to develop, release, or deliver any Material (defined below), code, or functionality. Having completed the GPU acceleration of one or more components of the application it is possible to compare the outcome with the original expectation. 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. The latter case can be avoided by using single-precision floating-point constants, defined with an f suffix such as 3.141592653589793f, 1.0f, 0.5f. Conversion to Warp isn't possible for Week 6-7 because there is no support for shared memory or block level synchronization. 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. 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. For some fractional exponents, exponentiation can be accelerated significantly compared to the use of pow() by using square roots, cube roots, and their inverses. The following table presents the evolution of matrix instruction sizes and supported data types for Tensor Cores across different GPU architecture generations. Memory optimizations are the most important area for performance. Therefore, any memory load or store of n addresses that spans b distinct memory banks can be serviced simultaneously, yielding an effective bandwidth that is b times as high as the bandwidth of a single bank. These are the primary hardware differences between CPU hosts and GPU devices with respect to parallel programming. Aside from memory bank conflicts, there is no penalty for non-sequential or unaligned accesses by a warp in shared memory. 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. Flow control instructions (if, switch, do, for, while) can significantly affect the instruction throughput by causing threads of the same warp to diverge; that is, to follow different execution paths. To use other CUDA APIs introduced in a minor release (that require a new driver), one would have to implement fallbacks or fail gracefully. Missing dependencies is also a binary compatibility break, hence you should provide fallbacks or guards for functionality that depends on those interfaces. 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. To minimize bank conflicts, it is important to understand how memory addresses map to memory banks and how to optimally schedule memory requests. 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. Existing CUDA Applications within Minor Versions of CUDA, 15.4.1.1. All CUDA threads can access it for read and write. 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. 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 from any of the four 32-byte segments only a subset of the words are requested (e.g. 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. In the NVIDIA Ampere GPU architecture remote NVLINK accesses go through a Link TLB on the remote GPU. TO THE EXTENT NOT PROHIBITED BY LAW, IN NO EVENT WILL NVIDIA BE LIABLE FOR ANY DAMAGES, INCLUDING WITHOUT LIMITATION ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, PUNITIVE, OR CONSEQUENTIAL DAMAGES, HOWEVER CAUSED AND REGARDLESS OF THE THEORY OF LIABILITY, ARISING OUT OF ANY USE OF THIS DOCUMENT, EVEN IF NVIDIA HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. 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. Find centralized, trusted content and collaborate around the technologies you use most. If such an application is run on a system with the R418 driver installed, CUDA initialization will return an error as can be seen in the example below. (BFloat16 only supports FP32 as accumulator), unsigned char/signed char (8-bit precision). 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. The other three kernels in this example use dynamically allocated shared memory, which can be used when the amount of shared memory is not known at compile time. The read-only texture memory space is cached. 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. Other differences are discussed as they arise elsewhere in this document. The cudaEventElapsedTime() function returns the time elapsed between the recording of the start and stop events. Copyright 2020-2023, NVIDIA Corporation & Affiliates. 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. CUDA supports several compatibility choices: First introduced in CUDA 10, the CUDA Forward Compatible Upgrade is designed to allow users to get access to new CUDA features and run applications built with new CUDA releases on systems with older installations of the NVIDIA datacenter driver. What is a word for the arcane equivalent of a monastery? "After the incident", I started to be more careful not to trip over things. As such, the constant cache is best when threads in the same warp accesses only a few distinct locations. libcuda.so on Linux systems). likewise return their own sets of error codes. 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. 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. Execution Configuration Optimizations, 11.1.2. This is possible because the distribution of the warps across the block is deterministic as mentioned in SIMT Architecture of the CUDA C++ Programming Guide. It also disables single-precision denormal support and lowers the precision of single-precision division in general. For 32-bit applications, the file would be cublas32_55.dll. Minimize redundant accesses to global memory whenever possible. See the CUDA C++ Programming Guide for details. To ensure correct results when parallel threads cooperate, we must synchronize the threads. Starting with the Volta architecture, Independent Thread Scheduling allows a warp to remain diverged outside of the data-dependent conditional block. Strong scaling is a measure of how, for a fixed overall problem size, the time to solution decreases as more processors are added to a system. A copy kernel that illustrates misaligned accesses. When we can, we should use registers. 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. Figure 6 illustrates how threads in the CUDA device can access the different memory components. We can reuse this cache line in subsequent iterations of the loop, and we would eventually utilize all 8 words; however, when many warps execute on the same multiprocessor simultaneously, as is generally the case, the cache line may easily be evicted from the cache between iterations i and i+1. Many codes accomplish a significant portion of the work with a relatively small amount of code. Before implementing lower priority recommendations, it is good practice to make sure all higher priority recommendations that are relevant have already been applied. Instead, all instructions are scheduled, but a per-thread condition code or predicate controls which threads execute the instructions. For example, consider the following code: Here, the sub-expression stride*i could overflow a 32-bit integer, so if i is declared as unsigned, the overflow semantics prevent the compiler from using some optimizations that might otherwise have applied, such as strength reduction. For example, improving occupancy from 66 percent to 100 percent generally does not translate to a similar increase in performance. Performance benefits can be more readily achieved when this ratio is higher. Therefore, it is important to be sure to compare values of like precision and to express the results within a certain tolerance rather than expecting them to be exact. This is done with the FLDCW x86 assembly instruction or the equivalent operating system API. 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). Optimal global memory coalescing is achieved for both reads and writes because global memory is always accessed through the linear, aligned index t. The reversed index tr is only used to access shared memory, which does not have the sequential access restrictions of global memory for optimal performance. The goal is to maximize the use of the hardware by maximizing bandwidth. Theoretical bandwidth can be calculated using hardware specifications available in the product literature. It is recommended to use cudaMallocAsync() and cudaFreeAsync() which are stream ordered pool allocators to manage device memory. For GPUs with compute capability 8.6, shared memory capacity per SM is 100 KB. NVIDIA Ampere GPU Architecture Tuning, 1.4.1.2. When multiple threads in a block use the same data from global memory, shared memory can be used to access the data from global memory only once. In other words, a cubin object generated for compute capability X.y will only execute on devices of compute capability X.z where zy. The NVIDIA System Management Interface (nvidia-smi) is a command line utility that aids in the management and monitoring of NVIDIA GPU devices. A Sequential but Misaligned Access Pattern, 9.2.2.2. 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. Automatic variables that are likely to be placed in local memory are large structures or arrays that would consume too much register space and arrays that the compiler determines may be indexed dynamically. The first is the compute capability, and the second is the version number of the CUDA Runtime and CUDA Driver APIs. This does not mean that application binaries compiled using an older toolkit will not be supported anymore. Failure to do so could lead to too many resources requested for launch errors. Likewise, for exponentation with an exponent of -1/3, use rcbrt() or rcbrtf(). 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. A grid of N/w by M/w blocks is launched, where each thread block calculates the elements of a different tile in C from a single tile of A and a single tile of B. Block-column matrix multiplied by block-row matrix. Effective bandwidth is calculated by timing specific program activities and by knowing how data is accessed by the program. Under UVA, pinned host memory allocated with cudaHostAlloc() will have identical host and device pointers, so it is not necessary to call cudaHostGetDevicePointer() for such allocations. However, if multiple addresses of a memory request map to the same memory bank, the accesses are serialized. 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. No license, either expressed or implied, is granted under any NVIDIA patent right, copyright, or other NVIDIA intellectual property right under this document. Then, thread A wants to read Bs element from shared memory, and vice versa. Consequently, the order in which arithmetic operations are performed is important. To understand the effect of hitRatio and num_bytes, we use a sliding window micro benchmark. Current utilization rates are reported for both the compute resources of the GPU and the memory interface. Each generation of CUDA-capable device has an associated compute capability version that indicates the feature set supported by the device (see CUDA Compute Capability). Customer should obtain the latest relevant information before placing orders and should verify that such information is current and complete. However, as with APOD as a whole, program optimization is an iterative process (identify an opportunity for optimization, apply and test the optimization, verify the speedup achieved, and repeat), meaning that it is not necessary for a programmer to spend large amounts of time memorizing the bulk of all possible optimization strategies prior to seeing good speedups. 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. To achieve high memory bandwidth for concurrent accesses, shared memory is divided into equally sized memory modules (banks) that can be accessed simultaneously. The functions that make up the CUDA Runtime API are explained in the CUDA Toolkit Reference Manual. First, we set aside 30 MB of the L2 cache for persisting accesses using cudaDeviceSetLimit(), as discussed above. Another way to view occupancy is the percentage of the hardwares ability to process warps that is actively in use. Because shared memory is shared by threads in a thread block, it provides a mechanism for threads to cooperate. To obtain best performance in cases where the control flow depends on the thread ID, the controlling condition should be written so as to minimize the number of divergent warps. All of these products (nvidia-smi, NVML, and the NVML language bindings) are updated with each new CUDA release and provide roughly the same functionality. Because the size of the persistent memory region (freqSize * sizeof(int) bytes) is much, smaller than the size of the streaming memory region (dataSize * sizeof(int) bytes), data, in the persistent region is accessed more frequently*/, //Number of bytes for persisting accesses in range 10-60 MB, //Hint for cache hit ratio. An upgraded driver matching the CUDA runtime version is currently required for those APIs. Medium Priority: Prefer faster, more specialized math functions over slower, more general ones when possible. When using multiple GPUs from the same application, it is recommended to use GPUs of the same type, rather than mixing hardware generations. 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. The details of managing the accelerator device are handled implicitly by an OpenACC-enabled compiler and runtime. This action leads to a load of eight L2 cache segments per warp on the Tesla V100 (compute capability 7.0). The current GPU core temperature is reported, along with fan speeds for products with active cooling. Unified Shared Memory/L1/Texture Cache, NVIDIA Ampere GPU Architecture Compatibility Guide for CUDA Applications. GPUs with compute capability 8.6 support shared memory capacity of 0, 8, 16, 32, 64 or 100 KB per SM. It would have been more so if adjacent warps had not exhibited such a high degree of reuse of the over-fetched cache lines. However, it also can act as a constraint on occupancy. There are many such factors involved in selecting block size, and inevitably some experimentation is required. We will note some of them later on in the document. For Windows, the /DELAY option is used; this requires that the application call SetDllDirectory() before the first call to any CUDA API function in order to specify the directory containing the CUDA DLLs. The same goes for other CUDA Toolkit libraries: cuFFT has an interface similar to that of FFTW, etc. Applications compiled against a CUDA Toolkit version will only run on systems with the specified minimum driver version for that toolkit version. Obtaining the right answer is clearly the principal goal of all computation. 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. Now Let's Look at Shared Memory Common Programming Pattern (5.1.2 of CUDA manual) - Load data into shared memory - Synchronize (if necessary) - Operate on data in shared memory - Synchronize (if necessary) - Write intermediate results to global memory - Repeat until done Shared memory Global memory Familiar concept?? Applications using the new API can load the final device code directly using driver APIs cuModuleLoadData and cuModuleLoadDataEx. Furthermore, the need for context switching can reduce utilization when work from several contexts could otherwise execute concurrently (see also Concurrent Kernel Execution). We evaluate the performance of both kernels using elements of size 4B, 8B and 16B per thread i.e., using int, int2 and int4 for the template parameter. The remainder of the kernel code is identical to the staticReverse() kernel. 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. This kernel has an effective bandwidth of 144.4 GB/s on an NVIDIA Tesla V100. Because L2 cache is on-chip, it potentially provides higher bandwidth and lower latency accesses to global memory. TO THE EXTENT NOT PROHIBITED BY LAW, IN NO EVENT WILL NVIDIA BE LIABLE FOR ANY DAMAGES, INCLUDING WITHOUT LIMITATION ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, PUNITIVE, OR CONSEQUENTIAL DAMAGES, HOWEVER CAUSED AND REGARDLESS OF THE THEORY OF LIABILITY, ARISING OUT OF ANY USE OF THIS DOCUMENT, EVEN IF NVIDIA HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. Please see the MSDN documentation for these routines for more information. Cached in L1 and L2 by default except on devices of compute capability 5.x; devices of compute capability 5.x cache locals only in L2. Asking for help, clarification, or responding to other answers. Using the CUDA Occupancy Calculator to project GPU multiprocessor occupancy. Low Priority: Avoid automatic conversion of doubles to floats. Using asynchronous copies does not use any intermediate register. Randomly accessing. Devices of compute capability 3.x have configurable bank size, which can be set using cudaDeviceSetSharedMemConfig() to either four bytes (cudaSharedMemBankSizeFourByte, thedefault) or eight bytes (cudaSharedMemBankSizeEightByte). It should also be noted that the CUDA math librarys complementary error function, erfcf(), is particularly fast with full single-precision accuracy.