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CUDA Toolkit is released on a monthly release cadence to deliver new features, performance improvements, and critical bug fixes. So while the impact is still evident it is not as large as we might have expected. This guide introduces the Assess, Parallelize, Optimize, Deploy(APOD) design cycle for applications with the goal of helping application developers to rapidly identify the portions of their code that would most readily benefit from GPU acceleration, rapidly realize that benefit, and begin leveraging the resulting speedups in production as early as possible. No license, either expressed or implied, is granted under any NVIDIA patent right, copyright, or other NVIDIA intellectual property right under this document. For example, on a device of compute capability 7.0, a kernel with 128-thread blocks using 37 registers per thread results in an occupancy of 75% with 12 active 128-thread blocks per multi-processor, whereas a kernel with 320-thread blocks using the same 37 registers per thread results in an occupancy of 63% because only four 320-thread blocks can reside on a multiprocessor. 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. Because L2 cache is on-chip, it potentially provides higher bandwidth and lower latency accesses to global memory. CUDA shared memory not faster than global? The major and minor revision numbers of the compute capability are shown on the seventh line of Figure 16. For some fractional exponents, exponentiation can be accelerated significantly compared to the use of pow() by using square roots, cube roots, and their inverses. However, the set of registers (known as the register file) is a limited commodity that all threads resident on a multiprocessor must share. If individual CUDA threads are copying elements of 16 bytes, the L1 cache can be bypassed. Performance benefits can be more readily achieved when this ratio is higher. Error counts are provided for both the current boot cycle and the lifetime of the GPU. Coalesced using shared memory to store a tile of A, Using shared memory to eliminate redundant reads of a tile of B. 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 cudaEventElapsedTime() function returns the time elapsed between the recording of the start and stop events. The following examples use the cuBLAS library from CUDA Toolkit 5.5 as an illustration: In a shared library on Linux, there is a string field called the SONAME that indicates the binary compatibility level of the library. The key here is that libraries are most useful when they match well with the needs of the application. In a typical system, thousands of threads are queued up for work (in warps of 32 threads each). Note that when a thread block allocates more registers than are available on a multiprocessor, the kernel launch fails, as it will when too much shared memory or too many threads are requested. This approach permits some overlapping of the data transfer and execution. and one element in the streaming data section. By using new CUDA versions, users can benefit from new CUDA programming model APIs, compiler optimizations and math library features. math libraries or deep learning frameworks) do not have a direct dependency on the CUDA runtime, compiler or driver. This guide refers to and relies on several other documents that you should have at your disposal for reference, all of which are available at no cost from the CUDA website https://docs.nvidia.com/cuda/. The results of these optimizations are summarized in Table 3. These are the primary hardware differences between CPU hosts and GPU devices with respect to parallel programming. 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. FP16 / FP32 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. Ensure global memory accesses are coalesced. A Sequential but Misaligned Access Pattern, 9.2.2.2. In addition to the calculator spreadsheet, occupancy can be determined using the NVIDIA Nsight Compute Profiler. For other algorithms, implementations may be considered correct if they match the reference within some small epsilon. 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. The NVIDIA Ampere GPU architecture adds hardware acceleration for copying data from global memory to shared memory. When accessing uncached local or global memory, there are hundreds of clock cycles of memory latency. 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. In such cases, kernels with 32x32 or 64x16 threads can be launched with each thread processing four elements of the shared memory array. 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. Non-default streams are required for this overlap because memory copy, memory set functions, and kernel calls that use the default stream begin only after all preceding calls on the device (in any stream) have completed, and no operation on the device (in any stream) commences until they are finished. The current GPU core temperature is reported, along with fan speeds for products with active cooling. The CUDA compiler (nvcc), provides a way to handle CUDA and non-CUDA code (by splitting and steering compilation), along with the CUDA runtime, is part of the CUDA compiler toolchain. 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. 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(). We can then launch this kernel onto the GPU and retrieve the results without requiring major rewrites to the rest of our application. The support for running numerous threads in parallel derives from CUDAs use of a lightweight threading model described above. Minimize redundant accesses to global memory whenever possible. Is it possible to share a Cuda context between applications - Introduction CUDA is a parallel computing platform and programming model created by Nvidia. Consequently, the order in which arithmetic operations are performed is important. 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). Because the driver may interleave execution of CUDA calls from other non-default streams, calls in other streams may be included in the timing. 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. For small integer powers (e.g., x2 or x3), explicit multiplication is almost certainly faster than the use of general exponentiation routines such as pow(). This Link TLB has a reach of 64 GB to the remote GPUs memory. For regions of system memory that have already been pre-allocated, cudaHostRegister() can be used to pin the memory on-the-fly without the need to allocate a separate buffer and copy the data into it. 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. 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. Choosing execution parameters is a matter of striking a balance between latency hiding (occupancy) and resource utilization. If this happens, the different execution paths must be executed separately; this increases the total number of instructions executed for this warp. The following table presents the evolution of matrix instruction sizes and supported data types for Tensor Cores across different GPU architecture generations. This is possible because the distribution of the warps across the block is deterministic as mentioned in SIMT Architecture of the CUDA C++ Programming Guide. Actions that present substantial improvements for most CUDA applications have the highest priority, while small optimizations that affect only very specific situations are given a lower priority. Non-default streams (streams other than stream 0) are required for concurrent execution because kernel calls that use the default stream begin only after all preceding calls on the device (in any stream) have completed, and no operation on the device (in any stream) commences until they are finished. A natural decomposition of the problem is to use a block and tile size of wxw threads. Each floating-point arithmetic operation involves a certain amount of rounding. The interface is augmented to retrieve either the PTX or cubin if an actual architecture is specified. However, compared to cache based architectures, like CPUs, latency hiding architectures, like GPUs, tend to cope better with completely random memory access patterns. Each threadblock would do the work it needs to (e.g. If textures are fetched using tex1D(),tex2D(), or tex3D() rather than tex1Dfetch(), the hardware provides other capabilities that might be useful for some applications such as image processing, as shown in Table 4. The performance of the sliding-window benchmark with fixed hit-ratio of 1.0. 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. 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(). -use_fast_math compiler option of nvcc coerces every functionName() call to the equivalent __functionName() call. With UVA, the host memory and the device memories of all installed supported devices share a single virtual address space. How many blocks can be allocated if i use shared memory? So there is no chance of memory corruption caused by overcommitting shared memory. A noteworthy exception to this are completely random memory access patterns. We define binary compatibility as a set of guarantees provided by the library, where an application targeting the said library will continue to work when dynamically linked against a different version of the library. As illustrated in Figure 7, non-unit-stride global memory accesses should be avoided whenever possible. Verify that your library doesnt leak dependencies, breakages, namespaces, etc. This chapter discusses the various kinds of memory on the host and device and how best to set up data items to use the memory effectively. In order to profit from any modern processor architecture, GPUs included, the first steps are to assess the application to identify the hotspots, determine whether they can be parallelized, and understand the relevant workloads both now and in the future. Theoretical bandwidth can be calculated using hardware specifications available in the product literature. Support for Bfloat16 Tensor Core, through HMMA instructions. 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. They produce equivalent results. Hardware utilization can also be improved in some cases by designing your application so that multiple, independent kernels can execute at the same time. 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. There is a total of 64 KB constant memory on a device. Going a step further, if most functions are defined as __host__ __device__ rather than just __device__ functions, then these functions can be tested on both the CPU and the GPU, thereby increasing our confidence that the function is correct and that there will not be any unexpected differences in the results. Using asynchronous copies does not use any intermediate register. PTX programs are translated at load time to the target hardware instruction set via the JIT Compiler which is part of the CUDA driver. The NVIDIA Ampere GPU architecture increases the capacity of the L2 cache to 40 MB in Tesla A100, which is 7x larger than Tesla V100. In CUDA there is no defined global synchronization mechanism except the kernel launch. The hardware splits a conflicting memory request into as many separate conflict-free requests as necessary, decreasing the effective bandwidth by a factor equal to the number of colliding memory requests. Reading from a texture while writing to its underlying global memory array in the same kernel launch should be avoided because the texture caches are read-only and are not invalidated when the associated global memory is modified. Then, thread A wants to read Bs element from shared memory, and vice versa. For further details on the programming features discussed in this guide, please refer to the CUDA C++ Programming Guide. If the GPU must wait on one warp of threads, it simply begins executing work on another. But before executing this final line in which each thread accesses data in shared memory that was written by another thread, remember that we need to make sure all threads have completed the loads to shared memory, by calling__syncthreads(). 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. When redistributing the dynamically-linked versions of one or more CUDA libraries, it is important to identify the exact files that need to be redistributed. The NVIDIA Ampere GPU architecture allows CUDA users to control the persistence of data in L2 cache. CUDA Memory Global Memory We used global memory to hold the functions values. The CUDA driver ensures backward Binary Compatibility is maintained for compiled CUDA applications. 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. The cause of the difference is shared memory bank conflicts. Devices to be made visible to the application should be included as a comma-separated list in terms of the system-wide list of enumerable devices. There is no way to check this for a specific variable, but the compiler reports total local memory usage per kernel (lmem) when run with the--ptxas-options=-v option. The achieved bandwidth is approximately 790 GB/s. 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. An application can also use the Occupancy API from the CUDA Runtime, e.g. When choosing the first execution configuration parameter-the number of blocks per grid, or grid size - the primary concern is keeping the entire GPU busy. Comparing Performance of Synchronous vs Asynchronous Copy from Global Memory to Shared Memory. Automatic variables that are likely to be placed in local memory are large structures or arrays that would consume too much register space and arrays that the compiler determines may be indexed dynamically. Kernels can be written using the CUDA instruction set architecture, called PTX, which is described in the PTX reference manual. 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. 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. Devices of compute capability 1.3 and higher provide native support for double-precision floating-point values (that is, values 64 bits wide). Failure to do so could lead to too many resources requested for launch errors. For example, it may be desirable to use a 64x64 element shared memory array in a kernel, but because the maximum number of threads per block is 1024, it is not possible to launch a kernel with 64x64 threads per block. Timeline comparison for copy and kernel execution. On the other hand, some applications designs will require some amount of refactoring to expose their inherent parallelism. Therefore, an application that compiled successfully on an older version of the toolkit may require changes in order to compile against a newer version of the toolkit. Overall, developers can expect similar occupancy as on Volta without changes to their application. The CUDA Toolkits End-User License Agreement (EULA) allows for redistribution of many of the CUDA libraries under certain terms and conditions. Global memory loads and stores by threads of a warp are coalesced by the device into as few as possible transactions. From CUDA 11.3 NVRTC is also semantically versioned. High Priority: To maximize developer productivity, profile the application to determine hotspots and bottlenecks. 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. The NVIDIA Ampere GPU architecture adds hardware acceleration for a split arrive/wait barrier in shared memory. The NVIDIA System Management Interface (nvidia-smi) is a command line utility that aids in the management and monitoring of NVIDIA GPU devices. The L2 cache set-aside size for persisting accesses may be adjusted, within limits: Mapping of user data to L2 set-aside portion can be controlled using an access policy window on a CUDA stream or CUDA graph kernel node. 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. No contractual obligations are formed either directly or indirectly by this document. This is the case for: Functions operating on char or short whose operands generally need to be converted to an int, Double-precision floating-point constants (defined without any type suffix) used as input to single-precision floating-point computations. For some applications the problem size will remain constant and hence only strong scaling is applicable. In this code, two streams are created and used in the data transfer and kernel executions as specified in the last arguments of the cudaMemcpyAsync call and the kernels execution configuration. Because shared memory is shared by threads in a thread block, it provides a mechanism for threads to cooperate. 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. The NVIDIA Ampere GPU architecture retains and extends the same CUDA programming model provided by previous NVIDIA GPU architectures such as Turing and Volta, and applications that follow the best practices for those architectures should typically see speedups on the NVIDIA A100 GPU without any code changes. This does not apply to the NVIDIA Driver; the end user must still download and install an NVIDIA Driver appropriate to their GPU(s) and operating system. CUDA applications are built against the CUDA Runtime library, which handles device, memory, and kernel management. Randomly accessing. Recall that shared memory is local to each SM. On devices that have this capability, the overlap once again requires pinned host memory, and, in addition, the data transfer and kernel must use different, non-default streams (streams with non-zero stream IDs). No contractual obligations are formed either directly or indirectly by this document. Even a relatively slow kernel may be advantageous if it avoids one or more transfers between host and device memory. Moreover, in such cases, the argument-reduction code uses local memory, which can affect performance even more because of the high latency of local memory. The example below shows how to use the access policy window on a CUDA stream. Now, if 3/4 of the running time of a sequential program is parallelized, the maximum speedup over serial code is 1 / (1 - 3/4) = 4. 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. Both the CUDA driver and the CUDA runtime are not source compatible across the different SDK releases. Throughput Reported by Visual Profiler, 9.1. Examples include modeling fluids or structures as meshes or grids and some Monte Carlo simulations, where increasing the problem size provides increased accuracy. In such cases, users or developers can still benefit from not having to upgrade the entire CUDA Toolkit or driver to use these libraries or frameworks. For optimal performance, users should manually tune the NUMA characteristics of their application. .Y stands for the minor version - Introduction of new APIs, deprecation of old APIs, and source compatibility might be broken but binary compatibility is maintained. These results are substantially lower than the corresponding measurements for the C = AB kernel. TF32 provides 8-bit exponent, 10-bit mantissa and 1 sign-bit. Furthermore, there should be multiple active blocks per multiprocessor so that blocks that arent waiting for a __syncthreads() can keep the hardware busy. Because the minimum memory transaction size is larger than most word sizes, the actual memory throughput required for a kernel can include the transfer of data not used by the kernel. Salient Features of Device Memory, Misaligned sequential addresses that fall within five 32-byte segments, Adjacent threads accessing memory with a stride of 2, /* Set aside max possible size of L2 cache for persisting accesses */, // Stream level attributes data structure. 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. (See also the__launch_bounds__ qualifier discussed in Execution Configuration of the CUDA C++ Programming Guide to control the number of registers used on a per-kernel basis.). To use other CUDA APIs introduced in a minor release (that require a new driver), one would have to implement fallbacks or fail gracefully. The cubins are architecture-specific. To obtain best performance in cases where the control flow depends on the thread ID, the controlling condition should be written so as to minimize the number of divergent warps. It can be copied into the same directory as the application executable or into a subdirectory of that installation path.

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