cuda shared memory between blocks cuda shared memory between blocks

By leveraging the semantic versioning, starting with CUDA 11, components in the CUDA Toolkit will remain binary compatible across the minor versions of the toolkit. We can see this usage in the following example: NVRTC is a runtime compilation library for CUDA C++. 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. Why do academics stay as adjuncts for years rather than move around? When working with a feature exposed in a minor version of the toolkit, the feature might not be available at runtime if the application is running against an older CUDA driver. By using new CUDA versions, users can benefit from new CUDA programming model APIs, compiler optimizations and math library features. The access requirements for coalescing depend on the compute capability of the device and are documented in the CUDA C++ Programming Guide. The performance of the sliding-window benchmark with fixed hit-ratio of 1.0. CUDA Toolkit is released on a monthly release cadence to deliver new features, performance improvements, and critical bug fixes. There are many possible approaches to profiling the code, but in all cases the objective is the same: to identify the function or functions in which the application is spending most of its execution time. Setting the bank size to eight bytes can help avoid shared memory bank conflicts when accessing double precision data. TF32 is a new 19-bit Tensor Core format that can be easily integrated into programs for more accurate DL training than 16-bit HMMA formats. The key here is that libraries are most useful when they match well with the needs of the application. When using multiple GPUs from the same application, it is recommended to use GPUs of the same type, rather than mixing hardware generations. This is the default if using nvcc to link in CUDA 5.5 and later. 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. This new feature is exposed via the pipeline API in CUDA. While NVIDIA GPUs are frequently associated with graphics, they are also powerful arithmetic engines capable of running thousands of lightweight threads in parallel. These barriers can also be used alongside the asynchronous copy. This will ensure that the executable will be able to run even if the user does not have the same CUDA Toolkit installed that the application was built against. To do so, use this equation: \(\text{Effective\ bandwidth} = \left( {\left( B_{r} + B_{w} \right) \div 10^{9}} \right) \div \text{time}\). By comparison, the smallest executable unit of parallelism on a CUDA device comprises 32 threads (termed a warp of threads). Data Transfer Between Host and Device provides further details, including the measurements of bandwidth between the host and the device versus within the device proper. Compiler JIT Cache Management Tools, 18.1. This Best Practices Guide is a manual to help developers obtain the best performance from NVIDIA CUDA GPUs. The synchronous version for the kernel loads an element from global memory to an intermediate register and then stores the intermediate register value to shared memory. It can be copied into the same directory as the application executable or into a subdirectory of that installation path. What if you need multiple dynamically sized arrays in a single kernel? Testing of all parameters of each product is not necessarily performed by NVIDIA. The effective bandwidth of this kernel is 140.2 GB/s on an NVIDIA Tesla V100.These results are lower than those obtained by the final kernel for C = AB. The cudaGetDeviceProperties() function reports various features of the available devices, including the CUDA Compute Capability of the device (see also the Compute Capabilities section of the CUDA C++ Programming Guide). 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. 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. It is worth noting that several of the other functions in the above example also take up a significant portion of the overall running time, such as calcStats() and calcSummaryData(). 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. CUDA C++ provides a simple path for users familiar with the C++ programming language to easily write programs for execution by the device. If not, my suggestion would be to start by breaking your work into separate kernels, and using the kernel launch(es) as sync points. As an exception, scattered writes to HBM2 see some overhead from ECC but much less than the overhead with similar access patterns on ECC-protected GDDR5 memory. An explicit __syncwarp() can be used to guarantee that the warp has reconverged for subsequent instructions. A lower occupancy kernel will have more registers available per thread than a higher occupancy kernel, which may result in less register spilling to local memory; in particular, with a high degree of exposed instruction-level parallelism (ILP) it is, in some cases, possible to fully cover latency with a low occupancy. Support for Bfloat16 Tensor Core, through HMMA instructions. Managing your GPU cluster will help achieve maximum GPU utilization and help you and your users extract the best possible performance. 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. An exception is the case where all threads in a warp address the same shared memory address, resulting in a broadcast. Therefore, the total number of links available is increased to twelve in A100, versus six in V100, yielding 600 GB/s bidirectional bandwidth versus 300 GB/s for V100. Always check the error return values on all CUDA API functions, even for functions that are not expected to fail, as this will allow the application to detect and recover from errors as soon as possible should they occur. In CUDA there is no defined global synchronization mechanism except the kernel launch. 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. 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. (Note that on devices of Compute Capability 1.2 or later, the memory system can fully coalesce even the reversed index stores to global memory. The simple remedy is to pad the shared memory array so that it has an extra column, as in the following line of code. Reinitialize the GPU hardware and software state via a secondary bus reset. 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. The way to avoid strided access is to use shared memory as before, except in this case a warp reads a row of A into a column of a shared memory tile, as shown in An optimized handling of strided accesses using coalesced reads from global memory. Devices of compute capability 2.0 and later support a special addressing mode called Unified Virtual Addressing (UVA) on 64-bit Linux and Windows. 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). BFloat16 format is especially effective for DL training scenarios. Shared memory Bank Conflicts: Shared memory bank conflicts exist and are common for the strategy used. For other applications, the problem size will grow to fill the available processors. When using CPU timers, it is critical to remember that many CUDA API functions are asynchronous; that is, they return control back to the calling CPU thread prior to completing their work. As illustrated in Figure 7, non-unit-stride global memory accesses should be avoided whenever possible. Modern NVIDIA GPUs can support up to 2048 active threads concurrently per multiprocessor (see Features and Specifications of the CUDA C++ Programming Guide) On GPUs with 80 multiprocessors, this leads to more than 160,000 concurrently active threads. When using a shared or static library, follow the release notes of said library to determine if the library supports minor version compatibility. Another important concept is the management of system resources allocated for a particular task. 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. Declare shared memory in CUDA C/C++ device code using the__shared__variable declaration specifier. In the code in Zero-copy host code, kernel() can reference the mapped pinned host memory using the pointer a_map in exactly the same was as it would if a_map referred to a location in device memory. When JIT compilation of PTX device code is used, the NVIDIA driver caches the resulting binary code on disk. The major and minor revision numbers of the compute capability are shown on the seventh line of Figure 16. 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. The functions that make up the CUDA Runtime API are explained in the CUDA Toolkit Reference Manual. These are the same contexts used implicitly by the CUDA Runtime when there is not already a current context for a thread. Throughput Reported by Visual Profiler, 9.1. NVIDIA-SMI can be used to configure a GPU for exclusive process mode, which limits the number of contexts per GPU to one. 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. Also of note is the Thrust library, which is a parallel C++ template library similar to the C++ Standard Template Library. A device in which work is poorly balanced across the multiprocessors will deliver suboptimal performance. APOD is a cyclical process: initial speedups can be achieved, tested, and deployed with only minimal initial investment of time, at which point the cycle can begin again by identifying further optimization opportunities, seeing additional speedups, and then deploying the even faster versions of the application into production. 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. Misaligned sequential addresses that fall within five 32-byte segments, Memory allocated through the CUDA Runtime API, such as via cudaMalloc(), is guaranteed to be aligned to at least 256 bytes. For exponentiation with an exponent of 1/3, use the cbrt() or cbrtf() function rather than the generic exponentiation functions pow() or powf(), as the former are significantly faster than the latter. These are the primary hardware differences between CPU hosts and GPU devices with respect to parallel programming. Replacing broken pins/legs on a DIP IC package. Overall, developers can expect similar occupancy as on Volta without changes to their application. 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 actual memory throughput shows how close the code is to the hardware limit, and a comparison of the effective or requested bandwidth to the actual bandwidth presents a good estimate of how much bandwidth is wasted by suboptimal coalescing of memory accesses (see Coalesced Access to Global Memory). The cause of the difference is shared memory bank conflicts. Weaknesses in customers product designs may affect the quality and reliability of the NVIDIA product and may result in additional or different conditions and/or requirements beyond those contained in this document. Making statements based on opinion; back them up with references or personal experience. //Set the attributes to a CUDA stream of type cudaStream_t, Mapping Persistent data accesses to set-aside L2 in sliding window experiment, /*Each CUDA thread accesses one element in the persistent data section. Asynchronous copy achieves better performance in nearly all cases. The performance of the sliding-window benchmark with tuned hit-ratio. Using Shared Memory in CUDA C/C++ | NVIDIA Technical Blog CUDA Refresher: The CUDA Programming Model - NVIDIA Technical Blog The application should also maximize parallel execution at a higher level by explicitly exposing concurrent execution on the device through streams, as well as maximizing concurrent execution between the host and the device. Asking for help, clarification, or responding to other answers. As a result, Thrust can be utilized in rapid prototyping of CUDA applications, where programmer productivity matters most, as well as in production, where robustness and absolute performance are crucial. It is also the only way for applications to run on devices that did not exist at the time the application was compiled. On GPUs with GDDR memory with ECC enabled the available DRAM is reduced by 6.25% to allow for the storage of ECC bits. 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. Floating Point Math Is not Associative, 8.2.3. Results obtained using double-precision arithmetic will frequently differ from the same operation performed via single-precision arithmetic due to the greater precision of the former and due to rounding issues. For example, if the threads of a warp access adjacent 4-byte words (e.g., adjacent float values), four coalesced 32-byte transactions will service that memory access. (To determine the latter number, see the deviceQuery CUDA Sample or refer to Compute Capabilities in the CUDA C++ Programming Guide.) High Priority: Ensure global memory accesses are coalesced whenever possible. See the Application Note on CUDA for Tegra for details. 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. It is important to use the same divisor when calculating theoretical and effective bandwidth so that the comparison is valid. The details of various CPU timing approaches are outside the scope of this document, but developers should always be aware of the resolution their timing calls provide. All CUDA threads can access it for read and write. Global, local, and texture memory have the greatest access latency, followed by constant memory, shared memory, and the register file. Using Shared Memory in CUDA Fortran | NVIDIA Technical Blog Using these data items, the peak theoretical memory bandwidth of the NVIDIA Tesla V100 is 898 GB/s: \(\left. The issue here is the number of operations performed per data element transferred. For an existing project, the first step is to assess the application to locate the parts of the code that are responsible for the bulk of the execution time. The maximum number of registers per thread can be set manually at compilation time per-file using the -maxrregcount option or per-kernel using the __launch_bounds__ qualifier (see Register Pressure). Some metric related to the number of active warps on a multiprocessor is therefore important in determining how effectively the hardware is kept busy. All threads within one block see the same shared memory array . Its important to note that both numbers are useful. NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any time without notice. See Math Libraries. The host system and the device each have their own distinct attached physical memories 1. APIs can be deprecated and removed. Best practices suggest that this optimization be performed after all higher-level optimizations have been completed. 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. 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. On Wednesday, February 19, 2020, NVIDIA will present part 2 of a 9-part CUDA Training Series titled "CUDA Shared Memory". Choosing execution parameters is a matter of striking a balance between latency hiding (occupancy) and resource utilization. For recent versions of CUDA hardware, misaligned data accesses are not a big issue. 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. The compiler can optimize groups of 4 load and store instructions. As PTX is compiled by the CUDA driver, new toolchains will generate PTX that is not compatible with the older CUDA driver. The compiler will perform these conversions if n is literal. Access to shared memory is much faster than global memory access because it is located on a chip. Threads can access data in shared memory loaded from global memory by other threads within the same thread block. CUDA 11.0 introduces an async-copy feature that can be used within device code to explicitly manage the asynchronous copying of data from global memory to shared memory. From the performance chart, the following observations can be made for this experiment. Is it suspicious or odd to stand by the gate of a GA airport watching the planes? 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. To learn more, see our tips on writing great answers. 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. Because separate registers are allocated to all active threads, no swapping of registers or other state need occur when switching among GPU threads. Awareness of how instructions are executed often permits low-level optimizations that can be useful, especially in code that is run frequently (the so-called hot spot in a program). For example, we can write our CUDA kernels as a collection of many short __device__ functions rather than one large monolithic __global__ function; each device function can be tested independently before hooking them all together. --ptxas-options=-v or -Xptxas=-v lists per-kernel register, shared, and constant memory usage. and one element in the streaming data section. Applying Strong and Weak Scaling, 6.3.2. Finally, this product is divided by 109 to convert the result to GB/s. The results of these optimizations are summarized in Table 3. Once we have located a hotspot in our applications profile assessment and determined that custom code is the best approach, we can use CUDA C++ to expose the parallelism in that portion of our code as a CUDA kernel. Is a PhD visitor considered as a visiting scholar? Therefore, in terms of wxw tiles, A is a column matrix, B is a row matrix, and C is their outer product; see Figure 11. NVLink operates transparently within the existing CUDA model. For further details on the programming features discussed in this guide, please refer to the CUDA C++ Programming Guide. This can be configured during runtime API from the host for all kernelsusing cudaDeviceSetCacheConfig() or on a per-kernel basis using cudaFuncSetCacheConfig(). Does there exist a square root of Euler-Lagrange equations of a field? Non-default streams are required for this overlap because memory copy, memory set functions, and 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. Whether a device has this capability is indicated by the asyncEngineCount field of the cudaDeviceProp structure (or listed in the output of the deviceQuery CUDA Sample). 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. 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. The last argument to the cudaMemcpyAsync() function is the stream ID, which in this case uses the default stream, stream 0. In many applications, a combination of strong and weak scaling is desirable. We define source compatibility as a set of guarantees provided by the library, where a well-formed application built against a specific version of the library (using the SDK) will continue to build and run without errors when a newer version of the SDK is installed. Strong scaling is usually equated with Amdahls Law, which specifies the maximum speedup that can be expected by parallelizing portions of a serial program. An Efficient Matrix Transpose in CUDA C/C++, How to Access Global Memory Efficiently in CUDA C/C++ Kernels, How to Access Global Memory Efficiently in CUDA Fortran Kernels, Top Video Streaming and Conferencing Sessions at NVIDIA GTC 2023, Top Cybersecurity Sessions at NVIDIA GTC 2023, Top Conversational AI Sessions at NVIDIA GTC 2023, Top AI Video Analytics Sessions at NVIDIA GTC 2023, Top Data Science Sessions at NVIDIA GTC 2023. Operations in different streams can be interleaved and in some cases overlapped - a property that can be used to hide data transfers between the host and the device. 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 CUDA only threads and the host can access memory. As can be seen from these tables, judicious use of shared memory can dramatically improve performance. The following sections discuss some caveats and considerations. For example, if you link against the CUDA 11.1 dynamic runtime, and use functionality from 11.1, as well as a separate shared library that was linked against the CUDA 11.2 dynamic runtime that requires 11.2 functionality, the final link step must include a CUDA 11.2 or newer dynamic runtime. NVIDIA Ampere GPU Architecture Tuning Guide, 1.4. (See Data Transfer Between Host and Device.) Like Volta, the NVIDIA Ampere GPU architecture combines the functionality of the L1 and texture caches into a unified L1/Texture cache which acts as a coalescing buffer for memory accesses, gathering up the data requested by the threads of a warp prior to delivery of that data to the warp. Essentially, it states that the maximum speedup S of a program is: Here P is the fraction of the total serial execution time taken by the portion of code that can be parallelized and N is the number of processors over which the parallel portion of the code runs. Staged concurrent copy and execute shows how the transfer and kernel execution can be broken up into nStreams stages. See https://developer.nvidia.com/nvidia-management-library-nvml for additional information. (The performance advantage sinpi() has over sin() is due to simplified argument reduction; the accuracy advantage is because sinpi() multiplies by only implicitly, effectively using an infinitely precise mathematical rather than a single- or double-precision approximation thereof.). A place where magic is studied and practiced? The performance of the kernels is shown in Figure 14. 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. Register pressure occurs when there are not enough registers available for a given task. Weak Scaling and Gustafsons Law, 3.1.3.3. Because shared memory is shared by threads in a thread block, it provides a mechanism for threads to cooperate. Is it possible to share a Cuda context between applications This padding eliminates the conflicts entirely, because now the stride between threads is w+1 banks (i.e., 33 for current devices), which, due to modulo arithmetic used to compute bank indices, is equivalent to a unit stride. Concurrent copy and execute demonstrates how to overlap kernel execution with asynchronous data transfer. Pinned memory should not be overused. On Systems on a Chip with integrated GPUs, such as NVIDIA Tegra, host and device memory are physically the same, but there is still a logical distinction between host and device memory.