Applying Strong and Weak Scaling, 6.3.2. 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. On parallel systems, it is possible to run into difficulties not typically found in traditional serial-oriented programming. 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. Local memory is so named because its scope is local to the thread, not because of its physical location. The CUDA Toolkit Samples provide several helper functions for error checking with the various CUDA APIs; these helper functions are located in the samples/common/inc/helper_cuda.h file in the CUDA Toolkit. This cost has several ramifications: The complexity of operations should justify the cost of moving data to and from the device. Shared Memory in Matrix Multiplication (C=AB), 9.2.3.3. Production code should, however, systematically check the error code returned by each API call and check for failures in kernel launches by calling cudaGetLastError(). As described in Memory Optimizations of this guide, bandwidth can be dramatically affected by the choice of memory in which data is stored, how the data is laid out and the order in which it is accessed, as well as other factors. For example, to use only devices 0 and 2 from the system-wide list of devices, set CUDA_VISIBLE_DEVICES=0,2 before launching the application. While compiler optimization improvements continually seek to narrow this gap, explicit multiplication (or the use of an equivalent purpose-built inline function or macro) can have a significant advantage. Armed with this knowledge, the developer can evaluate these bottlenecks for parallelization and start to investigate GPU acceleration. Programmers must primarily focus on following those recommendations to achieve the best performance. Checking these things frequently as an integral part of our cyclical APOD process will help ensure that we achieve the desired results as rapidly as possible. Copyright 2007-2023, NVIDIA Corporation & Affiliates. See Version Management for details on how to query the available CUDA software API versions. In CUDA there is no defined global synchronization mechanism except the kernel launch. The performance guidelines and best practices described in the CUDA C++ Programming Guide and the CUDA C++ Best Practices Guide apply to all CUDA-capable GPU architectures. See Building for Maximum Compatibility for further discussion of the flags used for building code for multiple generations of CUDA-capable device simultaneously. In particular, a larger block size does not imply a higher occupancy. Hardware Acceleration for Split Arrive/Wait Barrier, 1.4.1.4. --ptxas-options=-v or -Xptxas=-v lists per-kernel register, shared, and constant memory usage. The compiler can optimize groups of 4 load and store instructions. We fix the num_bytes in the access window to 20 MB and tune the hitRatio such that a random 20 MB of the total persistent data is resident in the L2 set-aside cache portion. To execute code on devices of specific compute capability, an application must load binary or PTX code that is compatible with this compute capability. Accesses to the remaining data of the memory region (i.e., streaming data) are considered normal or streaming accesses and will thus use the remaining 10 MB of the non set-aside L2 portion (unless part of the L2 set-aside portion is unused). For example, improving occupancy from 66 percent to 100 percent generally does not translate to a similar increase in performance. The following table presents the evolution of matrix instruction sizes and supported data types for Tensor Cores across different GPU architecture generations. In calculating each of the rows of a tile of matrix C, the entire tile of B is read. Lets say that two threads A and B each load a data element from global memory and store it to shared memory. Warp level support for Reduction Operations, 1.4.2.1. The library should follow semantic rules and increment the version number when a change is made that affects this ABI contract. 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. Furthermore, there should be multiple active blocks per multiprocessor so that blocks that arent waiting for a __syncthreads() can keep the hardware busy. Concurrent copy and execute demonstrates how to overlap kernel execution with asynchronous data transfer. aims to make the expression of this parallelism as simple as possible, while simultaneously enabling operation on CUDA-capable GPUs designed for maximum parallel throughput. 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. Depending on the original code, this can be as simple as calling into an existing GPU-optimized library such as cuBLAS, cuFFT, or Thrust, or it could be as simple as adding a few preprocessor directives as hints to a parallelizing compiler. If the PTX is also not available, then the kernel launch will fail. For example, in the standard CUDA Toolkit installation, the files libcublas.so and libcublas.so.5.5 are both symlinks pointing to a specific build of cuBLAS, which is named like libcublas.so.5.5.x, where x is the build number (e.g., libcublas.so.5.5.17). A slightly related but important topic is one of application binary compatibility across GPU architectures in CUDA. 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. For more details on the new Tensor Core operations refer to the Warp Matrix Multiply section in the CUDA C++ Programming Guide. Depending on the value of the num_bytes parameter and the size of L2 cache, one may need to tune the value of hitRatio to avoid thrashing of L2 cache lines. On the other hand, some applications designs will require some amount of refactoring to expose their inherent parallelism. 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. Context switches (when two threads are swapped) are therefore slow and expensive. On PCIe x16 Gen3 cards, for example, pinned memory can attain roughly 12 GB/s transfer rates. This guide summarizes the ways that an application can be fine-tuned to gain additional speedups by leveraging the NVIDIA Ampere GPU architectures features.1. 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. Shared memory enables cooperation between threads in a block. Hence, the A100 GPU enables a single thread block to address up to 163 KB of shared memory and GPUs with compute capability 8.6 can address up to 99 KB of shared memory in a single thread block. This kernel has an effective bandwidth of 144.4 GB/s on an NVIDIA Tesla V100. In these cases, no warp can ever diverge. The PTX string generated by NVRTC can be loaded by cuModuleLoadData and cuModuleLoadDataEx. Having completed the GPU acceleration of one or more components of the application it is possible to compare the outcome with the original expectation. 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. 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. If we validate our addressing logic separately prior to introducing the bulk of the computation, then this will simplify any later debugging efforts. CUDA Compatibility Across Minor Releases, 15.4.1. Memory Access It then explores how bandwidth affects performance metrics and how to mitigate some of the challenges it poses. Minimize data transfers between the host and the device. A key aspect of correctness verification for modifications to any existing program is to establish some mechanism whereby previous known-good reference outputs from representative inputs can be compared to new results. Creating additional contexts incurs memory overhead for per-context data and time overhead for context switching. This metric is occupancy. 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. A kernel to illustrate non-unit stride data copy. In other words, a cubin object generated for compute capability X.y will only execute on devices of compute capability X.z where zy. Devices of compute capability 1.0 to 1.3 have 16 KB/Block, compute 2.0 onwards have 48 KB/Block shared memory by default. Register pressure occurs when there are not enough registers available for a given task. Shared memory is a powerful feature for writing well optimized CUDA code. The larger N is(that is, the greater the number of processors), the smaller the P/N fraction. In the code in Zero-copy host code, kernel() can reference the mapped pinned host memory using the pointer a_map in exactly the same was as it would if a_map referred to a location in device memory. Using shared memory to coalesce global reads. Scattered accesses increase ECC memory transfer overhead, especially when writing data to global memory. When statically linking to the CUDA Runtime, multiple versions of the runtime can peacably coexist in the same application process simultaneously; for example, if an application uses one version of the CUDA Runtime, and a plugin to that application is statically linked to a different version, that is perfectly acceptable, as long as the installed NVIDIA Driver is sufficient for both. A natural decomposition of the problem is to use a block and tile size of wxw threads. Furthermore, the need for context switching can reduce utilization when work from several contexts could otherwise execute concurrently (see also Concurrent Kernel Execution). Data should be kept on the device as long as possible. Weak Scaling and Gustafsons Law describes weak scaling, where the speedup is attained by growing the problem size. 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. When the persistent data region fits well into the 30 MB set-aside portion of the L2 cache, a performance increase of as much as 50% is observed. 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. The latter case can be avoided by using single-precision floating-point constants, defined with an f suffix such as 3.141592653589793f, 1.0f, 0.5f. 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. This is not a problem when PTX is used for future device compatibility (the most common case), but can lead to issues when used for runtime compilation. The same goes for other CUDA Toolkit libraries: cuFFT has an interface similar to that of FFTW, etc. NVIDIA Ampere GPU Architecture Tuning, 1.4.1.2. Tuning the Access Window Hit-Ratio, 9.2.3.2. In contrast with cudaMemcpy(), the asynchronous transfer version requires pinned host memory (see Pinned Memory), and it contains an additional argument, a stream ID. Both the CUDA driver and the CUDA runtime are not source compatible across the different SDK releases. Using these data items, the peak theoretical memory bandwidth of the NVIDIA Tesla V100 is 898 GB/s: \(\left. In the previous post, I looked at how global memory accesses by a group of threads can be coalesced into a single transaction, and howalignment and stride affect coalescing for various generations of CUDA hardware. The right value for minBlocksPerMultiprocessor should be determined using a detailed per kernel analysis. 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. See the CUDA C++ Programming Guide for further explanations and software requirements for UVA and P2P. Resources stay allocated to each thread until it completes its execution. In other words, the term local in the name does not imply faster access. libcuda.so on Linux systems). For more information please refer to the section on Async Copy in the CUDA C++ Programming Guide. Adjacent threads accessing memory with a stride of 2. It also disables single-precision denormal support and lowers the precision of single-precision division in general. Two types of runtime math operations are supported. 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. All CUDA threads can access it for read and write. This should be our first candidate function for parallelization. On the other hand, some applications designs will require some amount of refactoring to expose their inherent parallelism. Sometimes, the best optimization might even be to avoid any data transfer in the first place by simply recomputing the data whenever it is needed. All threads within one block see the same shared memory array . That is, a thread can safely read a memory location via texture if the location has been updated by a previous kernel call or memory copy, but not if it has been previously updated by the same thread or another thread within the same kernel call. 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. By using new CUDA versions, users can benefit from new CUDA programming model APIs, compiler optimizations and math library features. These situations are where in CUDA shared memory offers a solution. It presents established parallelization and optimization techniques and explains coding metaphors and idioms that can greatly simplify programming for CUDA-capable GPU architectures. 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. Threads copy the data from global memory to shared memory with the statement s[t] = d[t], and the reversal is done two lines later with the statement d[t] = s[tr]. In order to optimize the performance, when the size of the persistent data is more than the size of the set-aside L2 cache portion, we tune the num_bytes and hitRatio parameters in the access window as below. The device will record a timestamp for the event when it reaches that event in the stream. Low Priority: Use shift operations to avoid expensive division and modulo calculations. Unified memory: supports seamless access to buffers or objects from multiple GPUs and CPUs. Single-precision floats provide the best performance, and their use is highly encouraged. Shared memory Bank Conflicts: Shared memory bank conflicts exist and are common for the strategy used. For slightly better performance, however, they should instead be declared as signed. 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. Increment major versions when there are ABI breaking changes such as API deprecation and modifications. See the CUDA C++ Programming Guide for details. CUDA Binary (cubin) Compatibility, 15.4. Replace sin(*
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