Intermediate data structures should be created in device memory, operated on by the device, and destroyed without ever being mapped by the host or copied to host memory. Current GPUs can simultaneously process asynchronous data transfers and execute kernels. For example, transferring two matrices to the device to perform a matrix addition and then transferring the results back to the host will not realize much performance benefit. The results of the various optimizations are summarized in Table 2. All kernel launches are asynchronous, as are memory-copy functions with the Async suffix on their names. Although the CUDA Runtime provides the option of static linking, some libraries included in the CUDA Toolkit are available only in dynamically-linked form. Can airtags be tracked from an iMac desktop, with no iPhone? For some fractional exponents, exponentiation can be accelerated significantly compared to the use of pow() by using square roots, cube roots, and their inverses. The difference between the phonemes /p/ and /b/ in Japanese. cudaStreamSynchronize() blocks the CPU thread until all CUDA calls previously issued into the given stream have completed. It then explores how bandwidth affects performance metrics and how to mitigate some of the challenges it poses. The -use_fast_math compiler option of nvcc coerces every functionName() call to the equivalent __functionName() call. 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. Therefore, it is best to avoid multiple contexts per GPU within the same CUDA application. (BFloat16 only supports FP32 as accumulator), unsigned char/signed char (8-bit precision). 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. Compiler JIT Cache Management Tools, 18.1. To use other CUDA APIs introduced in a minor release (that require a new driver), one would have to implement fallbacks or fail gracefully. Otherwise, five 32-byte segments are loaded per warp, and we would expect approximately 4/5th of the memory throughput achieved with no offsets. x86 processors can use an 80-bit double extended precision math when performing floating-point calculations. Can this be done? Figure 4 corresponds to this misalignments) The effective bandwidth for the copy with various offsets on an NVIDIA Tesla V100 (compute capability 7.0) is shown in Figure 5. Note this switch is effective only on single-precision floating point. 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. Medium Priority: Use shared memory to avoid redundant transfers from global memory. Access to shared memory is much faster than global memory access because it is located on chip. 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. Another common approach to parallelization of sequential codes is to make use of parallelizing compilers. The NVIDIA Ampere GPU architecture adds hardware acceleration for copying data from global memory to shared memory. Other differences are discussed as they arise elsewhere in this document. CUDA supports several compatibility choices: First introduced in CUDA 10, the CUDA Forward Compatible Upgrade is designed to allow users to get access to new CUDA features and run applications built with new CUDA releases on systems with older installations of the NVIDIA datacenter driver. 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. Furthermore, there should be multiple active blocks per multiprocessor so that blocks that arent waiting for a __syncthreads() can keep the hardware busy. 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. When you parallelize computations, you potentially change the order of operations and therefore the parallel results might not match sequential results. Host memory allocations pinned after-the-fact via cudaHostRegister(), however, will continue to have different device pointers than their host pointers, so cudaHostGetDevicePointer() remains necessary in that case. What is a word for the arcane equivalent of a monastery? This limitation is not specific to CUDA, but an inherent part of parallel computation on floating-point values. The effective bandwidth can vary by an order of magnitude depending on the access pattern for each type of memory. Then with a tile size of 32, the shared memory buffer will be of shape [32, 32]. 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. If all threads of a warp access the same location, then constant memory can be as fast as a register access. The maximum number of concurrent warps per SM remains the same as in Volta (i.e., 64), and other factors influencing warp occupancy are: The register file size is 64K 32-bit registers per SM. Delays in rolling out new NVIDIA drivers could mean that users of such systems may not have access to new features available in CUDA releases. This optimization is especially important for global memory accesses, because latency of access costs hundreds of clock cycles. (This was the default and only option provided in CUDA versions 5.0 and earlier.). CUDA shared memory not faster than global? This is done by carefully choosing the execution configuration of each kernel launch. Notwithstanding any damages that customer might incur for any reason whatsoever, NVIDIAs aggregate and cumulative liability towards customer for the products described herein shall be limited in accordance with the Terms of Sale for the product. Prior to UVA, an application had to keep track of which pointers referred to device memory (and for which device) and which referred to host memory as a separate bit of metadata (or as hard-coded information in the program) for each pointer. For example, the ability to overlap kernel execution with asynchronous data transfers between the host and the device is available on most but not all GPUs irrespective of the compute capability. CUDA Toolkit and Minimum Driver Versions. For example, on IBM Newell POWER9 nodes (where the CPUs correspond to NUMA nodes 0 and 8), use: One of the keys to good performance is to keep the multiprocessors on the device as busy as possible. These examples assume compute capability 6.0 or higher and that accesses are for 4-byte words, unless otherwise noted. Developers are notified through deprecation and documentation mechanisms of any current or upcoming changes. This suggests trading precision for speed when it does not affect the end result, such as using intrinsics instead of regular functions or single precision instead of double precision. 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. CUDA reserves 1 KB of shared memory per thread block. For certain devices of compute capability 5.2, L1-caching of accesses to global memory can be optionally enabled. 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. Several third-party debuggers support CUDA debugging as well; see: https://developer.nvidia.com/debugging-solutions for more details. Note also that whenever sine and cosine of the same argument are computed, the sincos family of instructions should be used to optimize performance: __sincosf() for single-precision fast math (see next paragraph). Unified Shared Memory/L1/Texture Cache, NVIDIA Ampere GPU Architecture Compatibility Guide for CUDA Applications. For devices of compute capability 2.x, there are two settings, 48KBshared memory / 16KB L1 cache, and 16KB shared memory / 48KB L1 cache. When using multiple GPUs from the same application, it is recommended to use GPUs of the same type, rather than mixing hardware generations. For example, to compute the effective bandwidth of a 2048 x 2048 matrix copy, the following formula could be used: \(\text{Effective\ bandwidth} = \left( {\left( 2048^{2} \times 4 \times 2 \right) \div 10^{9}} \right) \div \text{time}\). How to time code using CUDA events illustrates their use. The compute capability describes the features of the hardware and reflects the set of instructions supported by the device as well as other specifications, such as the maximum number of threads per block and the number of registers per multiprocessor. Is it possible to share a Cuda context between applications - Introduction CUDA is a parallel computing platform and programming model created by Nvidia. 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. This chapter contains a summary of the recommendations for optimization that are explained in this document. The reciprocal square root should always be invoked explicitly as rsqrtf() for single precision and rsqrt() for double precision. The following sections discuss some caveats and considerations. Local memory is so named because its scope is local to the thread, not because of its physical location. By reversing the array using shared memory we are able to have all global memory reads and writes performed with unit stride, achieving full coalescing on any CUDA GPU. :class table-no-stripes, Table 3. If L1-caching is enabled on these devices, the number of required transactions is equal to the number of required 128-byte aligned segments. CUDA driver - User-mode driver component used to run CUDA applications (e.g. High Priority: Ensure global memory accesses are coalesced whenever possible. These results are substantially lower than the corresponding measurements for the C = AB kernel. CUDA reserves 1 KB of shared memory per thread block. Devices of compute capability 1.3 and higher provide native support for double-precision floating-point values (that is, values 64 bits wide). What if you need multiple dynamically sized arrays in a single kernel? The SONAME of the library against which the application was built must match the filename of the library that is redistributed with the application. 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 devices of compute capability 1.x, the warp size is 32 threads and the number of banks is 16. Resources stay allocated to each thread until it completes its execution. In this scenario, CUDA initialization returns an error due to the minimum driver requirement. The CUDA Driver API has a versioned C-style ABI, which guarantees that applications that were running against an older driver (for example CUDA 3.2) will still run and function correctly against a modern driver (for example one shipped with CUDA 11.0). Another benefit of its union with shared memory, similar to Volta L1 is improvement in terms of both latency and bandwidth. The maximum number of thread blocks per SM is 32 for devices of compute capability 8.0 (i.e., A100 GPUs) and 16 for GPUs with compute capability 8.6. It is however usually more effective to use a high-level programming language such as C++. A copy kernel that illustrates misaligned accesses. This is because some operations common to each element can be performed by the thread once, amortizing the cost over the number of shared memory elements processed by the thread. Performance benefits can be more readily achieved when this ratio is higher. For this example, it is assumed that the data transfer and kernel execution times are comparable. To illustrate the effect of strided access on effective bandwidth, see the kernel strideCopy() in A kernel to illustrate non-unit stride data copy, which copies data with a stride of stride elements between threads from idata to odata. Please see the MSDN documentation for these routines for more information. Dont expose ABI structures that can change. Along with the increased memory capacity, the bandwidth is increased by 72%, from 900 GB/s on Volta V100 to 1550 GB/s on A100. To prevent the compiler from allocating too many registers, use the -maxrregcount=N compiler command-line option (see nvcc) or the launch bounds kernel definition qualifier (see Execution Configuration of the CUDA C++ Programming Guide) to control the maximum number of registers to allocated per thread. The CUDA Runtime API provides developers with high-level C++ interface for simplified management of devices, kernel executions etc., While the CUDA driver API provides (CUDA Driver API) a low-level programming interface for applications to target NVIDIA hardware. Starting with CUDA 11.0, devices of compute capability 8.0 and above have the capability to influence persistence of data in the L2 cache. The size is implicitly determined from the third execution configuration parameter when the kernel is launched. An additional set of Perl and Python bindings are provided for the NVML API. Low Priority: Use zero-copy operations on integrated GPUs for CUDA Toolkit version 2.2 and later. It provides functions to handle the following: Interoperability with OpenGL and Direct3D. Shared memory is extremely fast, user managed, on-chip memory that can be used to share data between threads within a thread block. It is best to enable this option in most circumstances. . Constant memory used for data that does not change (i.e. 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. Production code should, however, systematically check the error code returned by each API call and check for failures in kernel launches by calling cudaGetLastError(). If this happens, the different execution paths must be executed separately; this increases the total number of instructions executed for this warp. NVIDIA makes no representation or warranty that products based on this document will be suitable for any specified use. This access pattern results in four 32-byte transactions, indicated by the red rectangles. As a result, a read from constant memory costs one memory read from device memory only on a cache miss; otherwise, it just costs one read from the constant cache. 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. See Compute Capability 5.x in the CUDA C++ Programming Guide for further details. Reproduction of information in this document is permissible only if approved in advance by NVIDIA in writing, reproduced without alteration and in full compliance with all applicable export laws and regulations, and accompanied by all associated conditions, limitations, and notices. Timeline comparison for copy and kernel execution, Table 1. The Perl bindings are provided via CPAN and the Python bindings via PyPI. 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. 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. To target specific versions of NVIDIA hardware and CUDA software, use the -arch, -code, and -gencode options of nvcc. While the contents can be used as a reference manual, you should be aware that some topics are revisited in different contexts as various programming and configuration topics are explored. Tuning CUDA Applications for NVIDIA Ampere GPU Architecture. On devices of compute capability 5.x or newer, each bank has a bandwidth of 32 bits every clock cycle, and successive 32-bit words are assigned to successive banks. If instead i is declared as signed, where the overflow semantics are undefined, the compiler has more leeway to use these optimizations. To execute any CUDA program, there are three main steps: Copy the input data from host memory to device memory, also known as host-to-device transfer. This is because the user could only allocate the CUDA static shared memory up to 48 KB. See Register Pressure. Throughput Reported by Visual Profiler, 9.1. Use several smaller thread blocks rather than one large thread block per multiprocessor if latency affects performance. The number of blocks in a grid should be larger than the number of multiprocessors so that all multiprocessors have at least one block to execute. Even a relatively slow kernel may be advantageous if it avoids one or more transfers between host and device memory. On devices with GDDR memory, accessing memory in a coalesced way is even more important when ECC is turned on. //Such that up to 20MB of data is resident. For other algorithms, implementations may be considered correct if they match the reference within some small epsilon. Indicate whether the NVIDIA driver stays loaded when no applications are connected to the GPU. They can be distinguished by their names: some have names with prepended underscores, whereas others do not (e.g., __functionName() versus functionName()). Then, thread A wants to read Bs element from shared memory, and vice versa. 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. 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. UVA is also a necessary precondition for enabling peer-to-peer (P2P) transfer of data directly across the PCIe bus or NVLink for supported GPUs in supported configurations, bypassing host memory. For some applications the problem size will remain constant and hence only strong scaling is applicable. However, bank conflicts occur when copying the tile from global memory into shared memory. 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. The simple remedy is to pad the shared memory array so that it has an extra column, as in the following line of code. For most purposes, the key point is that the larger the parallelizable portion P is, the greater the potential speedup. Fixed value 1.0, The performance of the sliding-window benchmark with fixed hit-ratio of 1.0. CUDA kernel and thread hierarchy If A, B, and C are floating-point values, (A+B)+C is not guaranteed to equal A+(B+C) as it is in symbolic math. 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. Within a kernel call, the texture cache is not kept coherent with respect to global memory writes, so texture fetches from addresses that have been written via global stores in the same kernel call return undefined data. 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. To allocate an array in shared memory we . See the CUDA C++ Programming Guide for details. For example, a matrix multiplication of the same matrices requires N3 operations (multiply-add), so the ratio of operations to elements transferred is O(N), in which case the larger the matrix the greater the performance benefit. 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. Other peculiarities of floating-point arithmetic are presented in Features and Technical Specifications of the CUDA C++ Programming Guide as well as in a whitepaper and accompanying webinar on floating-point precision and performance available from https://developer.nvidia.com/content/precision-performance-floating-point-and-ieee-754-compliance-nvidia-gpus. 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. Support for bitwise AND along with bitwise XOR which was introduced in Turing, through BMMA instructions. In these cases, no warp can ever diverge. 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. Note that Gustafsons Law assumes that the ratio of serial to parallel execution remains constant, reflecting additional cost in setting up and handling the larger problem. No contractual obligations are formed either directly or indirectly by this document. It enables GPU threads to directly access host memory. Inspection of the PTX assembly code (obtained by compiling with -ptx or -keep command-line options to nvcc) reveals whether a variable has been placed in local memory during the first compilation phases. Asynchronous transfers enable overlap of data transfers with computation in two different ways. HBM2 memories, on the other hand, provide dedicated ECC resources, allowing overhead-free ECC protection.2. To verify the exact DLL filename that the application expects to find at runtime, use the dumpbin tool from the Visual Studio command prompt: Once the correct library files are identified for redistribution, they must be configured for installation into a location where the application will be able to find them. More difficult to parallelize are applications with a very flat profile - i.e., applications where the time spent is spread out relatively evenly across a wide portion of the code base. 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. 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. The one exception here is when multiple threads in a warp address the same shared memory location, resulting in a broadcast. // (Must be less than cudaDeviceProp::accessPolicyMaxWindowSize), // Hint for L2 cache hit ratio for persisting accesses in the num_bytes region. The repeated reading of the B tile can be eliminated by reading it into shared memory once (Improvement by reading additional data into shared memory). Formulae for exponentiation by small fractions, Sample CUDA configuration data reported by deviceQuery, +-----------------------------------------------------------------------------+, |-------------------------------+----------------------+----------------------+, |===============================+======================+======================|, +-------------------------------+----------------------+----------------------+, |=============================================================================|, cudaDevAttrCanUseHostPointerForRegisteredMem, 1.3. Not the answer you're looking for? Examples include modeling fluids or structures as meshes or grids and some Monte Carlo simulations, where increasing the problem size provides increased accuracy. We can reuse this cache line in subsequent iterations of the loop, and we would eventually utilize all 8 words; however, when many warps execute on the same multiprocessor simultaneously, as is generally the case, the cache line may easily be evicted from the cache between iterations i and i+1. 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. To subscribe to this RSS feed, copy and paste this URL into your RSS reader.
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