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. In particular, there is no register-related reason to pack data into vector data types such as float4 or int4 types. -use_fast_math compiler option of nvcc coerces every functionName() call to the equivalent __functionName() call. The effective bandwidth for this kernel is 12.8 GB/s on an NVIDIA Tesla V100. This does not mean that application binaries compiled using an older toolkit will not be supported anymore. As mentioned in Occupancy, higher occupancy does not always equate to better performance. 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. The following complete code (available on GitHub) illustrates various methods of using shared memory. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, How Intuit democratizes AI development across teams through reusability. 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). In Unoptimized handling of strided accesses to global memory, the row-th, col-th element of C is obtained by taking the dot product of the row-th and col-th rows of A. if several threads had accessed the same word or if some threads did not participate in the access), the full segment is fetched anyway. NVLink operates transparently within the existing CUDA model. Effective bandwidth is calculated by timing specific program activities and by knowing how data is accessed by the program. 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. The high-priority recommendations from those guides are as follows: Find ways to parallelize sequential code. CUDA driver - User-mode driver component used to run CUDA applications (e.g. In Using shared memory to improve the global memory load efficiency in matrix multiplication, each element in a tile of A is read from global memory only once, in a fully coalesced fashion (with no wasted bandwidth), to shared memory. cudaDeviceSynchronize()blocks the calling CPU thread until all CUDA calls previously issued by the thread are completed. 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. 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. Using UVA, on the other hand, the physical memory space to which a pointer points can be determined simply by inspecting the value of the pointer using cudaPointerGetAttributes(). Each floating-point arithmetic operation involves a certain amount of rounding. It is however usually more effective to use a high-level programming language such as C++. The CUDA Toolkit libraries (cuBLAS, cuFFT, etc.) Lets assume that A and B are threads in two different warps. If from any of the four 32-byte segments only a subset of the words are requested (e.g. Users should refer to the CUDA headers and documentation for new CUDA APIs introduced in a release. It would have been more so if adjacent warps had not exhibited such a high degree of reuse of the over-fetched cache lines. Data transfers between the host and the device using cudaMemcpy() are blocking transfers; that is, control is returned to the host thread only after the data transfer is complete. 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). Customer should obtain the latest relevant information before placing orders and should verify that such information is current and complete. High Priority: Avoid different execution paths within the same warp. Timeline comparison for copy and kernel execution. It is customers sole responsibility to evaluate and determine the applicability of any information contained in this document, ensure the product is suitable and fit for the application planned by customer, and perform the necessary testing for the application in order to avoid a default of the application or the product. This means that even though an application source might need to be changed if it has to be recompiled against a newer CUDA Toolkit in order to use the newer features, replacing the driver components installed in a system with a newer version will always support existing applications and its functions. 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. NVIDIA makes no representation or warranty that products based on this document will be suitable for any specified use. 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. The texture cache is optimized for 2D spatial locality, so threads of the same warp that read texture addresses that are close together will achieve best performance. 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(). . In many applications, a combination of strong and weak scaling is desirable. 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. Optimizations can be applied at various levels, from overlapping data transfers with computation all the way down to fine-tuning floating-point operation sequences. Both correctable single-bit and detectable double-bit errors are reported. The hardware splits a memory request that has bank conflicts into as many separate conflict-free requests as necessary, decreasing the effective bandwidth by a factor equal to the number of separate memory requests. Page-locked or pinned memory transfers attain the highest bandwidth between the host and the device. Shared memory is a powerful feature for writing well optimized CUDA code. and one element in the streaming data section. For example, to compute the effective bandwidth of a 2048 x 2048 matrix copy, the following formula could be used: \(\text{Effective\ bandwidth} = \left( {\left( 2048^{2} \times 4 \times 2 \right) \div 10^{9}} \right) \div \text{time}\). It is also the only way for applications to run on devices that did not exist at the time the application was compiled. This approach permits some overlapping of the data transfer and execution. The NVIDIA Ampere GPU architecture is NVIDIAs latest architecture for CUDA compute applications. In addition to the calculator spreadsheet, occupancy can be determined using the NVIDIA Nsight Compute Profiler. In this case, no warp diverges because the controlling condition is perfectly aligned with the warps. 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 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. 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. As even CPU architectures will require exposing 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.) Performance optimization revolves around three basic strategies: Optimizing memory usage to achieve maximum memory bandwidth, Optimizing instruction usage to achieve maximum instruction throughput. 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. Binary compatibility for cubins is guaranteed from one compute capability minor revision to the next one, but not from one compute capability minor revision to the previous one or across major compute capability revisions. 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. Of these different memory spaces, global memory is the most plentiful; see Features and Technical Specifications of the CUDA C++ Programming Guide for the amounts of memory available in each memory space at each compute capability level. As illustrated in Figure 7, non-unit-stride global memory accesses should be avoided whenever possible. Other company and product names may be trademarks of the respective companies with which they are associated. 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. Having completed the GPU acceleration of one or more components of the application it is possible to compare the outcome with the original expectation. We cannot declare these directly, but small static allocations go . The number of registers available, the maximum number of simultaneous threads resident on each multiprocessor, and the register allocation granularity vary over different compute capabilities. Between 128 and 256 threads per block is a good initial range for experimentation with different block sizes. Asking for help, clarification, or responding to other answers. When sharing data between threads, we need to be careful to avoid race conditions, because while threads in a block run logically in parallel, not all threads can execute physically at the same time. If this happens, the different execution paths must be executed separately; this increases the total number of instructions executed for this warp. As a result, all modern processors require parallel code in order to achieve good utilization of their computational power. As with the previous section on library building recommendations, if using the CUDA runtime, we recommend linking to the CUDA runtime statically when building your application. Likewise, for exponentation with an exponent of -1/3, use rcbrt() or rcbrtf(). With wrap, x is replaced by frac(x) where frac(x) = x - floor(x). 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. However, based on what you've described here, your algorithm might be amenable to an approach similar to what is outlined in the threadfence reduction sample. Hence, its important to design your application to use threads and blocks in a way that maximizes hardware utilization and to limit practices that impede the free distribution of work. If no new features are used (or if they are used conditionally with fallbacks provided) youll be able to remain compatible. The NVIDIA Ampere GPU architecture allows CUDA users to control the persistence of data in L2 cache. For devices of compute capability 8.0 (i.e., A100 GPUs) shared memory capacity per SM is 164 KB, a 71% increase compared to V100s capacity of 96 KB. Devices of compute capability 2.0 and later support a special addressing mode called Unified Virtual Addressing (UVA) on 64-bit Linux and Windows. There are multiple ways to declare shared memory inside a kernel, depending on whether the amount of memory is known at compile time or at run time. The amount of performance benefit an application will realize by running on CUDA depends entirely on the extent to which it can be parallelized. Creating additional contexts incurs memory overhead for per-context data and time overhead for context switching. Now I have some problems. Increased Memory Capacity and High Bandwidth Memory, 1.4.2.2. Recall that the initial assess step allowed the developer to determine an upper bound for the potential speedup attainable by accelerating given hotspots. 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. Shared memory has the lifetime of a block. 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. Access to shared memory is much faster than global memory access because it is located on a chip. 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. 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. 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. Many codes accomplish a significant portion of the work with a relatively small amount of code. However, the device is based on a distinctly different design from the host system, and its important to understand those differences and how they determine the performance of CUDA applications in order to use CUDA effectively. If L1-caching is enabled on these devices, the number of required transactions is equal to the number of required 128-byte aligned segments. The formulas in the table below are valid for x >= 0, x != -0, that is, signbit(x) == 0. Medium Priority: Prefer faster, more specialized math functions over slower, more general ones when possible. 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. Memory optimizations are the most important area for performance. This microbenchmark uses a 1024 MB region in GPU global memory. See the CUDA C++ Programming Guide for details. 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). In other words, the term local in the name does not imply faster access. If it has, it will be declared using the .local mnemonic and accessed using the ld.local and st.local mnemonics. However, it is best to avoid accessing global memory whenever possible. 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. If the shared memory array size is known at compile time, as in the staticReverse kernel, then we can explicitly declare an array of that size, as we do with the array s. In this kernel, t and tr are the two indices representing the original and reverse order, respectively. Some calculations use 10243 instead of 109 for the final calculation. These barriers can be used to implement fine grained thread controls, producer-consumer computation pipeline and divergence code patterns in CUDA. As a result, it is recommended that first-time readers proceed through the guide sequentially. Here, the effective bandwidth is in units of GB/s, Br is the number of bytes read per kernel, Bw is the number of bytes written per kernel, and time is given in seconds. The NVIDIA Ampere GPU architecture adds native support for warp wide reduction operations for 32-bit signed and unsigned integer operands. Overall, best performance is achieved when using asynchronous copies with an element of size 8 or 16 bytes. By using new CUDA versions, users can benefit from new CUDA programming model APIs, compiler optimizations and math library features. Managing your GPU cluster will help achieve maximum GPU utilization and help you and your users extract the best possible performance. 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. The NVIDIA A100 GPU supports shared memory capacity of 0, 8, 16, 32, 64, 100, 132 or 164 KB per SM. 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.
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