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 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. Throughout this guide, Kepler refers to devices of compute capability 3.x, Maxwell refers to devices of compute capability 5.x, Pascal refers to device of compute capability 6.x, Volta refers to devices of compute capability 7.0, Turing refers to devices of compute capability 7.5, and NVIDIA Ampere GPU Architecture refers to devices of compute capability 8.x. A shared memory request for a warp is split into one request for the first half of the warp and one request for the second half of the warp. 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 data will thus use the L2 set-aside portion. rev2023.3.3.43278. 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. A portion of the L2 cache can be set aside for persistent accesses to a data region in global memory. 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. 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. In this case the shared memory allocation size per thread block must be specified (in bytes) using an optional third execution configuration parameter, as in the following excerpt. Shared memory is a powerful feature for writing well optimized CUDA code. A place where magic is studied and practiced? The kernel is executed within a loop in host code that varies the parameter offset from 0 to 32. BFloat16 format is especially effective for DL training scenarios. Other company and product names may be trademarks of the respective companies with which they are associated. Strong Scaling and Amdahls Law describes strong scaling, which allows us to set an upper bound for the speedup with a fixed problem size. 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. Note that the process used for validating numerical results can easily be extended to validate performance results as well. Programmers should be aware of two version numbers. 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. CUDA Toolkit Library Redistribution, 16.4.1.2. There is a total of 64 KB constant memory on a device. High Priority: Use the effective bandwidth of your computation as a metric when measuring performance and optimization benefits. 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). Another common approach to parallelization of sequential codes is to make use of parallelizing compilers. Staged concurrent copy and execute shows how the transfer and kernel execution can be broken up into nStreams stages. When using the driver APIs directly, we recommend using the new driver entry point access API (cuGetProcAddress) documented here: CUDA Driver API :: CUDA Toolkit Documentation. .Z stands for the release/patch version - new updates and patches will increment this. Register pressure occurs when there are not enough registers available for a given task. Increment major versions when there are ABI breaking changes such as API deprecation and modifications. 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. If the PTX is also not available, then the kernel launch will fail. An exception is the case where all threads in a warp address the same shared memory address, resulting in a broadcast. The major and minor revision numbers of the compute capability are shown on the seventh line of Figure 16. Likewise, for exponentation with an exponent of -1/3, use rcbrt() or rcbrtf(). The library should follow semantic rules and increment the version number when a change is made that affects this ABI contract. 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. It is recommended to use cudaMallocAsync() and cudaFreeAsync() which are stream ordered pool allocators to manage device memory. 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. By understanding the end-users requirements and constraints and by applying Amdahls and Gustafsons laws, the developer can determine the upper bound of performance improvement from acceleration of the identified portions of the application. This chapter examines issues that can affect the correctness of returned data and points to appropriate solutions. If not, my suggestion would be to start by breaking your work into separate kernels, and using the kernel launch(es) as sync points. An example is transposing [1209, 9] of any type and 32 tile size. 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. However, it also can act as a constraint on occupancy. TF32 provides 8-bit exponent, 10-bit mantissa and 1 sign-bit. The performance of the kernels is shown in Figure 14. Information published by NVIDIA regarding third-party products or services does not constitute a license from NVIDIA to use such products or services or a warranty or endorsement thereof. Increased L2 capacity and L2 Residency Controls, 1.4.2.3. 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. Register dependencies arise when an instruction uses a result stored in a register written by an instruction before it. The context is explicit in the CUDA Driver API but is entirely implicit in the CUDA Runtime API, which creates and manages contexts automatically. 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. In fact, shared memory latency is roughly 100x lower than uncached global memory latency (provided that there are no bank conflicts between the threads, which we will examine later in this post). Support for Bfloat16 Tensor Core, through HMMA instructions. 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. Table 2. 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. Devices of compute capability 2.0 and later support a special addressing mode called Unified Virtual Addressing (UVA) on 64-bit Linux and Windows. This chapter discusses how to correctly measure performance using CPU timers and CUDA events. This can be used to manage data caches, speed up high-performance cooperative parallel algorithms, and facilitate global memory coalescing in cases where it would otherwise not be possible. As compared to the lower-level CUDA Driver API, the CUDA Runtime greatly eases device management by providing implicit initialization, context management, and device code module management. These include threading issues, unexpected values due to the way floating-point values are computed, and challenges arising from differences in the way CPU and GPU processors operate. (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.). Having a semantically versioned ABI means the interfaces need to be maintained and versioned. Shared Memory in Matrix Multiplication (C=AAT), 9.2.3.4. Randomly accessing. For this reason, ensuring that as much as possible of the data in each cache line fetched is actually used is an important part of performance optimization of memory accesses on these devices. Consider the following kernel code and access window parameters, as the implementation of the sliding window experiment. By default the 48KBshared memory setting is used. The support for running numerous threads in parallel derives from CUDAs use of a lightweight threading model described above. This code reverses the data in a 64-element array using shared memory. 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. Assess, Parallelize, Optimize, Deploy, 3.1.3.1. Conversion to Warp isn't possible for Week 6-7 because there is no support for shared memory or block level synchronization. High Priority: Minimize the use of global memory. For other applications, the problem size will grow to fill the available processors. Access to shared memory is much faster than global memory access because it is located on chip. Handling New CUDA Features and Driver APIs, 15.4.1.4. Replace sin(*) with sinpi(), cos(*) with cospi(), and sincos(*) with sincospi(). The cudaGetDeviceProperties() function reports various features of the available devices, including the CUDA Compute Capability of the device (see also the Compute Capabilities section of the CUDA C++ Programming Guide). If this happens, the different execution paths must be executed separately; this increases the total number of instructions executed for this warp. For the preceding procedure, assuming matrices of size NxN, there are N2 operations (additions) and 3N2 elements transferred, so the ratio of operations to elements transferred is 1:3 or O(1). This capability (combined with thread synchronization) has a number of uses, such as user-managed data caches, high-performance cooperative parallel algorithms (parallel reductions, for example), and tofacilitate global memory coalescing in cases where it would otherwise not be possible. This document is not a commitment to develop, release, or deliver any Material (defined below), code, or functionality. The compiler can optimize groups of 4 load and store instructions. It can be copied into the same directory as the application executable or into a subdirectory of that installation path. Local memory is so named because its scope is local to the thread, not because of its physical location. Kernel access to global memory also should be minimized by maximizing the use of shared memory on the device. This approach will tend to provide the best results for the time invested and will avoid the trap of premature optimization. When an application is built for multiple compute capabilities simultaneously (using several instances of the -gencode flag to nvcc), the binaries for the specified compute capabilities are combined into the executable, and the CUDA Driver selects the most appropriate binary at runtime according to the compute capability of the present device. A useful counterpart to the reference comparisons described above is to structure the code itself in such a way that is readily verifiable at the unit level. Data copied from global memory to shared memory using asynchronous copy instructions can be cached in the L1 cache or the L1 cache can be optionally bypassed. outside your established ABI contract. It will not allow any other CUDA call to begin until it has completed.) These many-way bank conflicts are very expensive. Consequently, the order in which arithmetic operations are performed is important. See the CUDA C++ Programming Guide for details. If L1-caching is enabled on these devices, the number of required transactions is equal to the number of required 128-byte aligned segments. 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. This value is expressed in milliseconds and has a resolution of approximately half a microsecond. 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). Best performance with synchronous copy is achieved when the copy_count parameter is a multiple of 4 for all three element sizes. Floor returns the largest integer less than or equal to x. Here cudaEventRecord() is used to place the start and stop events into the default stream, stream 0. 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. See Registers for details. Certain hardware features are not described by the compute capability. If you want to communicate (i.e. With wrap, x is replaced by frac(x) where frac(x) = x - floor(x). To minimize bank conflicts, it is important to understand how memory addresses map to memory banks. The __pipeline_wait_prior(0) will wait until all the instructions in the pipe object have been executed. Asynchronous copies are hardware accelerated for NVIDIA A100 GPU. However, as with APOD as a whole, program optimization is an iterative process (identify an opportunity for optimization, apply and test the optimization, verify the speedup achieved, and repeat), meaning that it is not necessary for a programmer to spend large amounts of time memorizing the bulk of all possible optimization strategies prior to seeing good speedups. The NVIDIA Ampere GPU architectures Streaming Multiprocessor (SM) provides the following improvements over Volta and Turing. Access to shared memory is much faster than global memory access because it is located on a chip. 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. 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. 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. The access requirements for coalescing depend on the compute capability of the device and are documented in the CUDA C++ Programming Guide. The examples in this section have illustrated three reasons to use shared memory: To enable coalesced accesses to global memory, especially to avoid large strides (for general matrices, strides are much larger than 32), To eliminate (or reduce) redundant loads from global memory. 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(). Furthermore, the pinning of system memory is a heavyweight operation compared to most normal system memory allocations, so as with all optimizations, test the application and the systems it runs on for optimal performance parameters. The value of this field is propagated into an application built against the library and is used to locate the library of the correct version at runtime. For optimal performance, users should manually tune the NUMA characteristics of their application. This is shown in Figure 1. It can be simpler to view N as a very large number, which essentially transforms the equation into \(S = 1/(1 - P)\). 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. CUDA Shared Memory Capacity - Lei Mao's Log Book Various dynamic and static information is reported, including board serial numbers, PCI device IDs, VBIOS/Inforom version numbers and product names. 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. From supercomputers to mobile phones, modern processors increasingly rely on parallelism to provide performance. 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. Devices of compute capability 3.x allow a third setting of 32KB shared memory / 32KB L1 cache which can be obtained using the optioncudaFuncCachePreferEqual. Existing CUDA Applications within Minor Versions of CUDA, 15.4.1.1. 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. Indicate whether compute processes can run on the GPU and whether they run exclusively or concurrently with other compute processes. Mapping Persistent data accesses to set-aside L2 in sliding window experiment. A key concept in this effort is occupancy, which is explained in the following sections. The performance of the sliding-window benchmark with fixed hit-ratio of 1.0. Coalescing concepts are illustrated in the following simple examples. For example, we can write our CUDA kernels as a collection of many short __device__ functions rather than one large monolithic __global__ function; each device function can be tested independently before hooking them all together. 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. This also prevents array elements being repeatedly read from global memory if the same data is required several times. 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. likewise return their own sets of error codes. The interface is augmented to retrieve either the PTX or cubin if an actual architecture is specified. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Some calculations use 10243 instead of 109 for the final calculation. The first and simplest case of coalescing can be achieved by any CUDA-enabled device of compute capability 6.0 or higher: the k-th thread accesses the k-th word in a 32-byte aligned array. The way to avoid strided access is to use shared memory as before, except in this case a warp reads a row of A into a column of a shared memory tile, as shown in An optimized handling of strided accesses using coalesced reads from global memory. 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. Although each of these instructions is scheduled for execution, only the instructions with a true predicate are actually executed. 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. In the next post I will continue our discussion of shared memory by using it to optimize a matrix transpose. CUDA Toolkit is released on a monthly release cadence to deliver new features, performance improvements, and critical bug fixes. Applications already using other BLAS libraries can often quite easily switch to cuBLAS, for example, whereas applications that do little to no linear algebra will have little use for cuBLAS. Alternatively, NVIDIA provides an occupancy calculator in the form of an Excel spreadsheet that enables developers to hone in on the optimal balance and to test different possible scenarios more easily. For other algorithms, implementations may be considered correct if they match the reference within some small epsilon. No license, either expressed or implied, is granted under any NVIDIA patent right, copyright, or other NVIDIA intellectual property right under this document. Like all CUDA Runtime API functions, this function will fail gracefully and return cudaErrorNoDevice to the application if there is no CUDA-capable GPU or cudaErrorInsufficientDriver if there is not an appropriate version of the NVIDIA Driver installed. The cause of the difference is shared memory bank conflicts. In some instances, operations performed by automatic NUMA balancing may degrade the performance of applications running on NVIDIA GPUs. 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. "After the incident", I started to be more careful not to trip over things. As a result, Thrust can be utilized in rapid prototyping of CUDA applications, where programmer productivity matters most, as well as in production, where robustness and absolute performance are crucial. 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. An optimized handling of strided accesses using coalesced reads from global memory. (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.). Bandwidth - the rate at which data can be transferred - is one of the most important gating factors for performance. The host runtime component of the CUDA software environment can be used only by host functions. 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. If the GPU must wait on one warp of threads, it simply begins executing work on another. Thedriver will honor the specified preference except when a kernel requires more shared memory per thread block than available in the specified configuration. The cudaDeviceEnablePeerAccess() API call remains necessary to enable direct transfers (over either PCIe or NVLink) between GPUs. Such a pattern is shown in Figure 3. The two kernels are very similar, differing only in how the shared memory arrays are declared and how the kernels are invoked. What is CUDA memory? - Quora

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