It is however usually more effective to use a high-level programming language such as C++. Comparing Performance of Synchronous vs Asynchronous Copy from Global Memory to Shared Memory. These recommendations are categorized by priority, which is a blend of the effect of the recommendation and its scope. It is therefore best to redistribute the CUDA Runtime library with the application when using dynamic linking or else to statically link against the CUDA Runtime. High Priority: Use the effective bandwidth of your computation as a metric when measuring performance and optimization benefits. 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). Please note that new versions of nvidia-smi are not guaranteed to be backward-compatible with previous versions. An upgraded driver matching the CUDA runtime version is currently required for those APIs. A stream is simply a sequence of operations that are performed in order on the device. This makes the code run faster at the cost of diminished precision and accuracy. Figure 6 illustrates how threads in the CUDA device can access the different memory components. CUDA Memory Global Memory We used global memory to hold the functions values. Package managers facilitate this process but unexpected issues can still arise and if a bug is found, it necessitates a repeat of the above upgrade process. Consider a simple transpose of a [2048, 1024] matrix to [1024, 2048]. It is important to use the same divisor when calculating theoretical and effective bandwidth so that the comparison is valid. It is faster than global memory. 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 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. CUDA shared memory writes incur unexplainable long latency, CUDA atomic function usage with volatile shared memory. 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. 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. GPUs with compute capability 8.6 support shared memory capacity of 0, 8, 16, 32, 64 or 100 KB per SM. For a warp of threads, col represents sequential columns of the transpose of A, and therefore col*TILE_DIM represents a strided access of global memory with a stride of w, resulting in plenty of wasted bandwidth. 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. When using a shared or static library, follow the release notes of said library to determine if the library supports minor version compatibility. Access to shared memory is much faster than global memory access because it is located on chip. 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. See Registers for details. The access policy window requires a value for hitRatio and num_bytes. 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. Can anyone please tell me how to do these two operations? This illustrates the use of the shared memory as a user-managed cache when the hardware L1 cache eviction policy does not match up well with the needs of the application or when L1 cache is not used for reads from global memory. Is it possible to create a concave light? 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. There are a number of tools that can be used to generate the profile. Support for TF32 Tensor Core, through HMMA instructions. This kernel has an effective bandwidth of 144.4 GB/s on an NVIDIA Tesla V100. Formulae for exponentiation by small fractions, Sample CUDA configuration data reported by deviceQuery, +-----------------------------------------------------------------------------+, |-------------------------------+----------------------+----------------------+, |===============================+======================+======================|, +-------------------------------+----------------------+----------------------+, |=============================================================================|, cudaDevAttrCanUseHostPointerForRegisteredMem, 1.3. outside your established ABI contract. 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. Because the memory copy and the kernel both return control to the host immediately, the host function cpuFunction() overlaps their execution. These are the primary hardware differences between CPU hosts and GPU devices with respect to parallel programming. Block-column matrix (A) multiplied by block-row matrix (B) with resulting product matrix (C). Shared memory is a powerful feature for writing well optimized CUDA code. For this purpose, it requires mapped pinned (non-pageable) memory. This is because the user could only allocate the CUDA static shared memory up to 48 KB. For devices of compute capability 1.x, the warp size is 32 threads and the number of banks is 16. 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. If you want to communicate (i.e. 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. 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(). 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. 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. This makes the code run faster at the cost of diminished precision and accuracy. Increased Memory Capacity and High Bandwidth Memory, 1.4.2.2. 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. In such cases, and when the execution time (tE) exceeds the transfer time (tT), a rough estimate for the overall time is tE + tT/nStreams for the staged version versus tE + tT for the sequential version. However, this requires writing to shared memory in columns, and because of the use of wxw tiles in shared memory, this results in a stride between threads of w banks - every thread of the warp hits the same bank (Recall that w is selected as 32). A useful technique to determine the sensitivity of performance to occupancy is through experimentation with the amount of dynamically allocated shared memory, as specified in the third parameter of the execution configuration. ? This difference is illustrated in Figure 13. But this technique is still useful for other access patterns, as Ill show in the next post.). In this particular example, the offset memory throughput achieved is, however, approximately 9/10th, because adjacent warps reuse the cache lines their neighbors fetched. Instead, strategies can be applied incrementally as they are learned. 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. 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. By simply increasing this parameter (without modifying the kernel), it is possible to effectively reduce the occupancy of the kernel and measure its effect on performance. 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. When choosing the block size, it is important to remember that multiple concurrent blocks can reside on a multiprocessor, so occupancy is not determined by block size alone. Production code should, however, systematically check the error code returned by each API call and check for failures in kernel launches by calling cudaGetLastError(). With wrap, x is replaced by frac(x) where frac(x) = x - floor(x). For most purposes, the key point is that the larger the parallelizable portion P is, the greater the potential speedup. Handling New CUDA Features and Driver APIs, 15.4.1.4. As illustrated in Figure 7, non-unit-stride global memory accesses should be avoided whenever possible. See Building for Maximum Compatibility for further discussion of the flags used for building code for multiple generations of CUDA-capable device simultaneously. When a CUDA kernel accesses a data region in the global memory repeatedly, such data accesses can be considered to be persisting. The nature of simulating nature: A Q&A with IBM Quantum researcher Dr. Jamie We've added a "Necessary cookies only" option to the cookie consent popup. Creating additional contexts incurs memory overhead for per-context data and time overhead for context switching. To do so, use this equation: \(\text{Effective\ bandwidth} = \left( {\left( B_{r} + B_{w} \right) \div 10^{9}} \right) \div \text{time}\). For some applications the problem size will remain constant and hence only strong scaling is applicable. 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. The ideal scenario is one in which many threads perform a substantial amount of work. Likewise, for exponentation with an exponent of -1/3, use rcbrt() or rcbrtf(). Indicate whether the NVIDIA driver stays loaded when no applications are connected to the GPU. Examples include modeling fluids or structures as meshes or grids and some Monte Carlo simulations, where increasing the problem size provides increased accuracy. Static linking makes the executable slightly larger, but it ensures that the correct version of runtime library functions are included in the application binary without requiring separate redistribution of the CUDA Runtime library. Dealing with relocatable objects is not yet supported, therefore the cuLink* set of APIs in the CUDA driver will not work with enhanced compatibility. NVIDIA accepts no liability related to any default, damage, costs, or problem which may be based on or attributable to: (i) the use of the NVIDIA product in any manner that is contrary to this document or (ii) customer product designs. Low Priority: Use zero-copy operations on integrated GPUs for CUDA Toolkit version 2.2 and later. Using shared memory to coalesce global reads. Shared memory is extremely fast, user managed, on-chip memory that can be used to share data between threads within a thread block. What is CUDA memory? - Quora Load the GPU program and execute, caching data on-chip for performance. "After the incident", I started to be more careful not to trip over things. The available profiling tools are invaluable for guiding this process, as they can help suggest a next-best course of action for the developers optimization efforts and provide references into the relevant portions of the optimization section of this guide. 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. A simple implementation for C = AAT is shown in Unoptimized handling of strided accesses to global memory, Unoptimized handling of strided accesses to global memory. Is it suspicious or odd to stand by the gate of a GA airport watching the planes? Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. The most straightforward approach to parallelizing an application is to leverage existing libraries that take advantage of parallel architectures on our behalf. Sequential copy and execute and Staged concurrent copy and execute demonstrate this. The PTX string generated by NVRTC can be loaded by cuModuleLoadData and cuModuleLoadDataEx. Optimizing memory usage starts with minimizing data transfers between the host and the device because those transfers have much lower bandwidth than internal device data transfers. I think this pretty much implies that you are going to have the place the heads of each queue in global memory. High Priority: Minimize data transfer between the host and the device, even if it means running some kernels on the device that do not show performance gains when compared with running them on the host CPU. By describing your computation in terms of these high-level abstractions you provide Thrust with the freedom to select the most efficient implementation automatically. The bandwidthTest CUDA Sample shows how to use these functions as well as how to measure memory transfer performance. For example, Overlapping computation and data transfers demonstrates how host computation in the routine cpuFunction() is performed while data is transferred to the device and a kernel using the device is executed. 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). Developers are notified through deprecation and documentation mechanisms of any current or upcoming changes. Code that uses the warp shuffle operation, for example, must be compiled with -arch=sm_30 (or higher compute capability). The CUDA Toolkit libraries (cuBLAS, cuFFT, etc.) An application has no direct control over these bank conflicts. Therefore, choosing sensible thread block sizes, such as multiples of the warp size (i.e., 32 on current GPUs), facilitates memory accesses by warps that are properly aligned. CUDA C++ provides a simple path for users familiar with the C++ programming language to easily write programs for execution by the device. If no new features are used (or if they are used conditionally with fallbacks provided) youll be able to remain compatible. CUDA kernel and thread hierarchy Latency hiding and occupancy depend on the number of active warps per multiprocessor, which is implicitly determined by the execution parameters along with resource (register and shared memory) constraints. It may be different with non-unit-strided accesses, however, and this is a pattern that occurs frequently when dealing with multidimensional data or matrices. The key here is that libraries are most useful when they match well with the needs of the application. Shared memory is a powerful feature for writing well-optimized CUDA code. Please see the MSDN documentation for these routines for more information. On all CUDA-enabled devices, it is possible to overlap host computation with asynchronous data transfers and with device computations. By using new CUDA versions, users can benefit from new CUDA programming model APIs, compiler optimizations and math library features. This Best Practices Guide is a manual to help developers obtain the best performance from NVIDIA CUDA GPUs. 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. The issue here is the number of operations performed per data element transferred. 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. TF32 provides 8-bit exponent, 10-bit mantissa and 1 sign-bit. Not using intermediate registers can help reduce register pressure and can increase kernel occupancy. NVIDIA-SMI can be used to configure a GPU for exclusive process mode, which limits the number of contexts per GPU to one. Recall that shared memory is local to each SM. See Register Pressure. Functions following functionName() naming convention are slower but have higher accuracy (e.g., sinf(x) and expf(x)). 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. rev2023.3.3.43278. The example below shows how to use the access policy window on a CUDA stream. It can be copied into the same directory as the application executable or into a subdirectory of that installation path. CUDA Toolkit and Minimum Driver Versions. This is important for a number of reasons; for example, it allows the user to profit from their investment as early as possible (the speedup may be partial but is still valuable), and it minimizes risk for the developer and the user by providing an evolutionary rather than revolutionary set of changes to the application. The right value for minBlocksPerMultiprocessor should be determined using a detailed per kernel analysis. However, low occupancy always interferes with the ability to hide memory latency, resulting in performance degradation. The optimal NUMA tuning will depend on the characteristics and desired hardware affinities of each application and node, but in general applications computing on NVIDIA GPUs are advised to choose a policy that disables automatic NUMA balancing. As with the dynamically-linked version of the CUDA Runtime library, these libraries should be bundled with the application executable when distributing that application. Often this means the use of directives-based approaches, where the programmer uses a pragma or other similar notation to provide hints to the compiler about where parallelism can be found without needing to modify or adapt the underlying code itself. Providing the two argument version of __launch_bounds__(maxThreadsPerBlock,minBlocksPerMultiprocessor) can improve performance in some cases. Some will expect bitwise identical results, which is not always possible, especially where floating-point arithmetic is concerned; see Numerical Accuracy and Precision regarding numerical accuracy. Dynamic parallelism - passing contents of shared memory to spawned blocks? Failure to do so could lead to too many resources requested for launch errors. Applying Strong and Weak Scaling, 6.3.2. Accesses to different addresses by threads within a warp are serialized, thus the cost scales linearly with the number of unique addresses read by all threads within a warp. In fact, local memory is off-chip. NVRTC used to support only virtual architectures through the option -arch, since it was only emitting PTX. Shared memory is magnitudes faster to access than global memory. So, in the previous example, had the two matrices to be added already been on the device as a result of some previous calculation, or if the results of the addition would be used in some subsequent calculation, the matrix addition should be performed locally on the device. 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. Prior to CUDA 11.0, the minimum driver version for a toolkit was the same as the driver shipped with that version of the CUDA Toolkit. On devices with GDDR memory, accessing memory in a coalesced way is even more important when ECC is turned on. (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.). Hence, for best overall application performance, it is important to minimize data transfer between the host and the device, even if that means running kernels on the GPU that do not demonstrate any speedup compared with running them on the host CPU. Floating Point Math Is not Associative, 8.2.3. To keep the kernels simple, M and N are multiples of 32, since the warp size (w) is 32 for current devices. Its important to be aware that calling __syncthreads() in divergent code is undefined and can lead to deadlockall threads within a thread block must call __syncthreads() at the same point. Then, as shown in the figure below, we specify that the accesses to the first freqSize * sizeof(int) bytes of the memory region are persistent. Devices of compute capability 8.6 have 2x more FP32 operations per cycle per SM than devices of compute capability 8.0. A kernel to illustrate non-unit stride data copy. Shared memory enables cooperation between threads in a block. 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). For codes continuing to make use of PTX, in order to support compiling on an older driver, your code must be first transformed into device code via the static ptxjitcompiler library or NVRTC with the option of generating code for a specific architecture (e.g. Because shared memory is shared by threads in a thread block, it provides a mechanism for threads to cooperate. The following table presents the evolution of matrix instruction sizes and supported data types for Tensor Cores across different GPU architecture generations. What is the difference between CUDA shared memory and global - Quora The size is implicitly determined from the third execution configuration parameter when the kernel is launched. NVIDIA accepts no liability related to any default, damage, costs, or problem which may be based on or attributable to: (i) the use of the NVIDIA product in any manner that is contrary to this document or (ii) customer product designs. In this example, the deviceQuery sample is compiled with CUDA 11.1 and is run on a system with R418.
