Access to shared memory is much faster than global memory access because it is located on chip. Missing dependencies is also a binary compatibility break, hence you should provide fallbacks or guards for functionality that depends on those interfaces. Shared memory accesses, in counterpoint, are usually worth optimizing only when there exists a high degree of bank conflicts. Therefore, to accurately measure the elapsed time for a particular call or sequence of CUDA calls, it is necessary to synchronize the CPU thread with the GPU by calling cudaDeviceSynchronize() immediately before starting and stopping the CPU timer. After each change is made, ensure that the results match using whatever criteria apply to the particular algorithm. The two kernels are very similar, differing only in how the shared memory arrays are declared and how the kernels are invoked. CUDA compatibility allows users to update the latest CUDA Toolkit software (including the compiler, libraries, and tools) without requiring update to the entire driver stack. 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. Instead of a __syncthreads()synchronization barrier call, a __syncwarp() is sufficient after reading the tile of A into shared memory because only threads within the warp that write the data into shared memory read this data. In A copy kernel that illustrates misaligned accesses, data is copied from the input array idata to the output array, both of which exist in global memory. To maintain architectural compatibility, static shared memory allocations remain limited to 48 KB, and an explicit opt-in is also required to enable dynamic allocations above this limit. These many-way bank conflicts are very expensive. 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. 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. 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). What sort of strategies would a medieval military use against a fantasy giant? (BFloat16 only supports FP32 as accumulator), unsigned char/signed char (8-bit precision). The major and minor revision numbers of the compute capability are shown on the seventh line of Figure 16. 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. 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. Using the CUDA Occupancy Calculator to project GPU multiprocessor occupancy. To minimize bank conflicts, it is important to understand how memory addresses map to memory banks. 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. On discrete GPUs, mapped pinned memory is advantageous only in certain cases. To minimize bank conflicts, it is important to understand how memory addresses map to memory banks and how to optimally schedule memory requests. This is common for building applications that are GPU architecture, platform and compiler agnostic. 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. For example, if the install name of the cuBLAS library is given as @rpath/libcublas.5.5.dylib, then the library is version 5.5 and the copy of this library redistributed with the application must be named libcublas.5.5.dylib, even though only -lcublas (with no version number specified) is used at link time. Hardware utilization can also be improved in some cases by designing your application so that multiple, independent kernels can execute at the same time. Certain memory access patterns enable the hardware to coalesce groups of reads or writes of multiple data items into one operation. To learn more, see our tips on writing great answers. In order to maintain forward compatibility to future hardware and toolkits and to ensure that at least one thread block can run on an SM, developers should include the single argument __launch_bounds__(maxThreadsPerBlock) which specifies the largest block size that the kernel will be launched with. An exception is the case where all threads in a warp address the same shared memory address, resulting in a broadcast. When the latter is much lower than the former, design or implementation details are likely to reduce bandwidth, and it should be the primary goal of subsequent optimization efforts to increase it. For branches including just a few instructions, warp divergence generally results in marginal performance losses. For example, servers that have two 32 core processors can run only 64 threads concurrently (or small multiple of that if the CPUs support simultaneous multithreading). Overall Performance Optimization Strategies, https://developer.nvidia.com/nsight-visual-studio-edition, https://developer.nvidia.com/debugging-solutions, https://developer.nvidia.com/content/precision-performance-floating-point-and-ieee-754-compliance-nvidia-gpus, Asynchronous and Overlapping Transfers with Computation, CUDA Driver API :: CUDA Toolkit Documentation, dynamically-linked version of the CUDA Runtime library, Where to Install Redistributed CUDA Libraries, https://developer.nvidia.com/gpu-deployment-kit, https://developer.nvidia.com/nvidia-management-library-nvml, https://developer.nvidia.com/cluster-management. ? This chapter examines issues that can affect the correctness of returned data and points to appropriate solutions. Both of your questions imply some sort of global synchronization. The most important consideration with any profiling activity is to ensure that the workload is realistic - i.e., that information gained from the test and decisions based upon that information are relevant to real data. 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. 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. A CUDA device has a number of different memory components that are available to programmers - register, shared memory, local memory, global memory and constant memory. On Linux and Mac, the -rpath linker option should be used to instruct the executable to search its local path for these libraries before searching the system paths: It may be necessary to adjust the value of -ccbin to reflect the location of your Visual Studio installation. A pointer to a structure with a size embedded is a better solution. Devices of compute capability 1.0 to 1.3 have 16 KB/Block, compute 2.0 onwards have 48 KB/Block shared memory by default. Figure 6 illustrates such a situation; in this case, threads within a warp access words in memory with a stride of 2. The goal is to maximize the use of the hardware by maximizing bandwidth. CUDA devices use several memory spaces, which have different characteristics that reflect their distinct usages in CUDA applications. Data should be kept on the device as long as possible. Table 2. 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. Customer should obtain the latest relevant information before placing orders and should verify that such information is current and complete. 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. There's no way around this. However, once the size of this persistent data region exceeds the size of the L2 set-aside cache portion, approximately 10% performance drop is observed due to thrashing of L2 cache lines. It will not allow any other CUDA call to begin until it has completed.) Applications compiled with CUDA toolkit versions as old as 3.2 will run on newer drivers. Ensure global memory accesses are coalesced. Furthermore, this file should be installed into the @rpath of the application; see Where to Install Redistributed CUDA Libraries. Excessive use can reduce overall system performance because pinned memory is a scarce resource, but how much is too much is difficult to know in advance. From supercomputers to mobile phones, modern processors increasingly rely on parallelism to provide performance. In such cases, call cudaGetDeviceProperties() to determine whether the device is capable of a certain feature. 1) I need to select only k blocks of out m blocks whose heads of queue is minimum k elements out of m elements. By comparison, threads on GPUs are extremely lightweight. 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. We fix the num_bytes in the access window to 20 MB and tune the hitRatio such that a random 20 MB of the total persistent data is resident in the L2 set-aside cache portion. Timeline comparison for copy and kernel execution, Table 1. The for loop over i multiplies a row of A by a column of B, which is then written to C. The effective bandwidth of this kernel is 119.9 GB/s on an NVIDIA Tesla V100. All rights reserved. Refer to the CUDA Toolkit Release Notes for details for the minimum driver version and the version of the driver shipped with the toolkit. As with the dynamically-linked version of the CUDA Runtime library, these libraries should be bundled with the application executable when distributing that application. Almost all changes to code should be made in the context of how they affect bandwidth. Understanding Scaling discusses the potential benefit we might expect from such parallelization. However, for each iteration i, all threads in a warp read the same value from global memory for matrix A, as the index row*TILE_DIM+i is constant within a warp. Once the parallelism of the algorithm has been exposed, it needs to be mapped to the hardware as efficiently as possible. Do new devs get fired if they can't solve a certain bug? As a useful side effect, this strategy will allow us a means to reduce code duplication should we wish to include both CPU and GPU execution paths in our application: if the bulk of the work of our CUDA kernels is done in __host__ __device__ functions, we can easily call those functions from both the host code and the device code without duplication. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. 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. Global, local, and texture memory have the greatest access latency, followed by constant memory, shared memory, and the register file. by synchronization between blocks, i take it that you mean preserve the order of blocks there is at least 1 method that i can think of, that generally accomplishes this you can either push a sequence of block numbers into (global) memory, and have thread blocks base the block they process next on this sequence; the sequence is read via an atomic 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. 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. FP16 / FP32 Sequential copy and execute and Staged concurrent copy and execute demonstrate this. The example below shows how an existing example can be adapted to use the new features, guarded by the USE_CUBIN macro in this case: We recommend that the CUDA runtime be statically linked to minimize dependencies. exchange data) between threadblocks, the only method is to use global memory. Asynchronous Copy from Global Memory to Shared Memory, 10. Local memory is so named because its scope is local to the thread, not because of its physical location. A more robust approach is to selectively introduce calls to fast intrinsic functions only if merited by performance gains and where altered behavior can be tolerated. For example, a 64-bit application linked to cuBLAS 5.5 will look for cublas64_55.dll at runtime, so this is the file that should be redistributed with that application, even though cublas.lib is the file that the application is linked against. Handling New CUDA Features and Driver APIs, 15.4.1.4. 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. Shared memory is specified by the device architecture and is measured on per-block basis. The effective bandwidth for this kernel is 12.8 GB/s on an NVIDIA Tesla V100. The NVIDIA Ampere GPU architecture includes new Third Generation Tensor Cores that are more powerful than the Tensor Cores used in Volta and Turing SMs. -use_fast_math compiler option of nvcc coerces every functionName() call to the equivalent __functionName() call. Floor returns the largest integer less than or equal to x. C++-style convenience wrappers (cuda_runtime.h) built on top of the C-style functions. Note that the process used for validating numerical results can easily be extended to validate performance results as well. Note that no bank conflict occurs if only one memory location per bank is accessed by a half warp of threads. 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. 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. Not the answer you're looking for? All CUDA compute devices follow the IEEE 754 standard for binary floating-point representation, with some small exceptions. 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. But this technique is still useful for other access patterns, as Ill show in the next post.). With each generation of NVIDIA processors, new features are added to the GPU that CUDA can leverage. However, low occupancy always interferes with the ability to hide memory latency, resulting in performance degradation. This approach will greatly improve your understanding of effective programming practices and enable you to better use the guide for reference later. 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. Computing a row of a tile. Performance Improvements Optimizing C = AA, Comparing Synchronous vs Asynchronous Copy from Global Memory to Shared Memory, Comparing Performance of Synchronous vs Asynchronous Copy from Global Memory to Shared Memory, Table 4. Indicate whether the NVIDIA driver stays loaded when no applications are connected to the GPU. 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. 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. What if you need multiple dynamically sized arrays in a single kernel? The current board power draw and power limits are reported for products that report these measurements. Is it possible to create a concave light? As even CPU architectures require exposing this parallelism in order to improve or simply maintain the performance of sequential applications, the CUDA family of parallel programming languages (CUDA C++, CUDA Fortran, etc.) This approach will tend to provide the best results for the time invested and will avoid the trap of premature optimization. In this calculation, the memory clock rate is converted in to Hz, multiplied by the interface width (divided by 8, to convert bits to bytes) and multiplied by 2 due to the double data rate. Useful Features for tex1D(), tex2D(), and tex3D() Fetches, __launch_bounds__(maxThreadsPerBlock,minBlocksPerMultiprocessor), Using the CUDA Occupancy Calculator to project GPU multiprocessor occupancy, cudaOccupancyMaxActiveBlocksPerMultiprocessor, // When the program/library launches work, // When the program/library is finished with the context, Table 5. Both the CUDA driver and the CUDA runtime are not source compatible across the different SDK releases. Unlike the CUDA Driver, the CUDA Runtime guarantees neither forward nor backward binary compatibility across versions. Threads can access data in shared memory loaded from global memory by other threads within the same thread block. All kernel launches are asynchronous, as are memory-copy functions with the Async suffix on their names. 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. Texture references that are bound to CUDA arrays can be written to via surface-write operations by binding a surface to the same underlying CUDA array storage). The performance of the sliding-window benchmark with tuned hit-ratio. 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. .Z stands for the release/patch version - new updates and patches will increment this. See Register Pressure. 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 hereby expressly objects to applying any customer general terms and conditions with regards to the purchase of the NVIDIA product referenced in this document. These exceptions, which are detailed in Features and Technical Specifications of the CUDA C++ Programming Guide, can lead to results that differ from IEEE 754 values computed on the host system. Applications compiled against a CUDA Toolkit version will only run on systems with the specified minimum driver version for that toolkit version. In our experiment, we vary the size of this persistent data region from 10 MB to 60 MB to model various scenarios where data fits in or exceeds the available L2 set-aside portion of 30 MB. NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any time without notice. 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. CUDA C++ provides a simple path for users familiar with the C++ programming language to easily write programs for execution by the device. The NVIDIA Ampere GPU architecture allows CUDA users to control the persistence of data in L2 cache. The CUDA Driver API thus is binary-compatible (the OS loader can pick up a newer version and the application continues to work) but not source-compatible (rebuilding your application against a newer SDK might require source changes). CUDA provides a simple barrier synchronization primitive, __syncthreads(). For more information on this pragma, refer to the CUDA C++ Programming Guide. If you want to communicate (i.e. Built on top of these technologies are CUDA libraries, some of which are included in the CUDA Toolkit, while others such as cuDNN may be released independently of the CUDA Toolkit. Please see the MSDN documentation for these routines for more information. Like the other calls in this listing, their specific operation, parameters, and return values are described in the CUDA Toolkit Reference Manual. At a minimum, you would need some sort of selection process that can access the heads of each queue. However, compared to cache based architectures, like CPUs, latency hiding architectures, like GPUs, tend to cope better with completely random memory access patterns. Block-column matrix multiplied by block-row matrix. Because of this, the maximum speedup S of a program is: Another way of looking at Gustafsons Law is that it is not the problem size that remains constant as we scale up the system but rather the execution time. Otherwise, five 32-byte segments are loaded per warp, and we would expect approximately 4/5th of the memory throughput achieved with no offsets. An example would be modeling how two molecules interact with each other, where the molecule sizes are fixed. CUDA work occurs within a process space for a particular GPU known as a context. Testing of all parameters of each product is not necessarily performed by NVIDIA. By using new CUDA versions, users can benefit from new CUDA programming model APIs, compiler optimizations and math library features. 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. Medium Priority: Use shared memory to avoid redundant transfers from global memory. Detecting Hardware and Software Configuration. This Best Practices Guide is a manual to help developers obtain the best performance from NVIDIA CUDA GPUs. On devices of compute capability 2.x and 3.x, each multiprocessor has 64KB of on-chip memory that can be partitioned between L1 cache and shared memory. The performance of the sliding-window benchmark with tuned hit-ratio. CUDA Compatibility Developers Guide, 15.3.1. As such, the constant cache is best when threads in the same warp accesses only a few distinct locations. To execute code on devices of specific compute capability, an application must load binary or PTX code that is compatible with this compute capability. The async-copy does not require the copy_count parameter to be a multiple of 4, to maximize performance through compiler optimizations. Alternatively, the nvcc command-line option -arch=sm_XX can be used as a shorthand equivalent to the following more explicit -gencode= command-line options described above: However, while the -arch=sm_XX command-line option does result in inclusion of a PTX back-end target by default (due to the code=compute_XX target it implies), it can only specify a single target cubin architecture at a time, and it is not possible to use multiple -arch= options on the same nvcc command line, which is why the examples above use -gencode= explicitly. It provides functions to handle the following: Interoperability with OpenGL and Direct3D. Medium Priority: Use the fast math library whenever speed trumps precision. 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. Coalescing concepts are illustrated in the following simple examples. One of several factors that determine occupancy is register availability. Can anyone please tell me how to do these two operations? Therefore, the compiler can optimize more aggressively with signed arithmetic than it can with unsigned arithmetic. Note that the NVIDIA Tesla A100 GPU has 40 MB of total L2 cache capacity. The CUDA driver ensures backward Binary Compatibility is maintained for compiled CUDA applications. Similarly, the single-precision functions sinpif(), cospif(), and sincospif() should replace calls to sinf(), cosf(), and sincosf() when the function argument is of the form *. 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. x86 processors can use an 80-bit double extended precision math when performing floating-point calculations. If it has, it will be declared using the .local mnemonic and accessed using the ld.local and st.local mnemonics. This variant simply uses the transpose of A in place of B, so C = AAT. If x is the coordinate and N is the number of texels for a one-dimensional texture, then with clamp, x is replaced by 0 if x < 0 and by 1-1/N if 1