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CUDA Memory Rules Currently can only transfer data from host to global (and constant memory) and not host directly to shared. 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. Not all threads need to participate. Mutually exclusive execution using std::atomic? The NVIDIA Management Library (NVML) is a C-based interface that provides direct access to the queries and commands exposed via nvidia-smi intended as a platform for building 3rd-party system management applications. 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. The ldd tool is useful for identifying the exact filenames of the libraries that the application expects to find at runtime as well as the path, if any, of the copy of that library that the dynamic loader would select when loading the application given the current library search path: In a shared library on Mac OS X, there is a field called the install name that indicates the expected installation path and filename the library; the CUDA libraries also use this filename to indicate binary compatibility. The issue here is the number of operations performed per data element transferred. When a CUDA kernel accesses a data region in the global memory repeatedly, such data accesses can be considered to be persisting. 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. Constantly recompiling with the latest CUDA Toolkit means forcing upgrades on the end-customers of an application product. 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. Tuning the Access Window Hit-Ratio, 9.2.3.2. THIS DOCUMENT AND ALL NVIDIA DESIGN SPECIFICATIONS, REFERENCE BOARDS, FILES, DRAWINGS, DIAGNOSTICS, LISTS, AND OTHER DOCUMENTS (TOGETHER AND SEPARATELY, MATERIALS) ARE BEING PROVIDED AS IS. NVIDIA MAKES NO WARRANTIES, EXPRESSED, IMPLIED, STATUTORY, OR OTHERWISE WITH RESPECT TO THE MATERIALS, AND EXPRESSLY DISCLAIMS ALL IMPLIED WARRANTIES OF NONINFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE. Assess, Parallelize, Optimize, Deploy, 3.1.3.1. Performance Improvements Optimizing C = AB Matrix Multiply Increased L2 capacity and L2 Residency Controls, 1.4.2.3. Here cudaEventRecord() is used to place the start and stop events into the default stream, stream 0. Medium Priority: Prefer faster, more specialized math functions over slower, more general ones when possible. 2) In one block I need to load into shared memory the queues of other blocks. This code reverses the data in a 64-element array using shared memory. This variant simply uses the transpose of A in place of B, so C = AAT. On Systems on a Chip with integrated GPUs, such as NVIDIA Tegra, host and device memory are physically the same, but there is still a logical distinction between host and device memory. Both of your questions imply some sort of global synchronization. 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. 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. I'm not sure if this will fit your overall processing. Now I have some problems. 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 incident has nothing to do with me; can I use this this way? It presents established parallelization and optimization techniques and explains coding metaphors and idioms that can greatly simplify programming for CUDA-capable GPU architectures. Strong scaling is a measure of how, for a fixed overall problem size, the time to solution decreases as more processors are added to a system. Figure 6 illustrates such a situation; in this case, threads within a warp access words in memory with a stride of 2. The various principal traits of the memory types are shown in Table 1. Cached in L1 and L2 by default on devices of compute capability 6.0 and 7.x; cached only in L2 by default on devices of lower compute capabilities, though some allow opt-in to caching in L1 as well via compilation flags. Memory optimizations are the most important area for performance. After this change, the effective bandwidth is 199.4 GB/s on an NVIDIA Tesla V100, which is comparable to the results from the last C = AB kernel. High Priority: To maximize developer productivity, profile the application to determine hotspots and bottlenecks. 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. For more details refer to the memcpy_async section in the CUDA C++ Programming Guide. All CUDA threads can access it for read and write. This ensures your code is compatible. The NVIDIA Nsight Visual Studio Edition for Microsoft Windows 7, Windows HPC Server 2008, Windows 8.1, and Windows 10 is available as a free plugin for Microsoft Visual Studio; see: https://developer.nvidia.com/nsight-visual-studio-edition. This technique could be used when the data dependency is such that the data can be broken into chunks and transferred in multiple stages, launching multiple kernels to operate on each chunk as it arrives. 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. For global memory accesses, this comparison of requested memory bandwidth to actual memory bandwidth is reported by the Global Memory Load Efficiency and Global Memory Store Efficiency metrics. 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. 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. The NVML API is shipped with the CUDA Toolkit (since version 8.0) and is also available standalone on the NVIDIA developer website as part of the GPU Deployment Kit through a single header file accompanied by PDF documentation, stub libraries, and sample applications; see https://developer.nvidia.com/gpu-deployment-kit. Such a pattern is shown in Figure 3. As for optimizing instruction usage, the use of arithmetic instructions that have low throughput should be avoided. // Number of bytes for persisting accesses. A shared memory request for a warp is not split as with devices of compute capability 1.x, meaning that bank conflicts can occur between threads in the first half of a warp and threads in the second half of the same warp. 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. Each floating-point arithmetic operation involves a certain amount of rounding. Asynchronous and Overlapping Transfers with Computation, 9.2.1.2. 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. GPUs with compute capability 8.6 support shared memory capacity of 0, 8, 16, 32, 64 or 100 KB per SM. 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. Shared memory is a powerful feature for writing well-optimized CUDA code. CUDA reserves 1 KB of shared memory per thread block. For most purposes, the key point is that the larger the parallelizable portion P is, the greater the potential speedup. If B has not finished writing its element before A tries to read it, we have a race condition, which can lead to undefined behavior and incorrect results. Some will expect bitwise identical results, which is not always possible, especially where floating-point arithmetic is concerned; see Numerical Accuracy and Precision regarding numerical accuracy. Formulae for exponentiation by small fractions, Sample CUDA configuration data reported by deviceQuery, +-----------------------------------------------------------------------------+, |-------------------------------+----------------------+----------------------+, |===============================+======================+======================|, +-------------------------------+----------------------+----------------------+, |=============================================================================|, cudaDevAttrCanUseHostPointerForRegisteredMem, 1.3. 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. The cudaGetDeviceCount() function can be used to query for the number of available devices. Concurrent copy and execute illustrates the basic technique. The effective bandwidth can vary by an order of magnitude depending on the access pattern for each type of memory. We define source compatibility as a set of guarantees provided by the library, where a well-formed application built against a specific version of the library (using the SDK) will continue to build and run without errors when a newer version of the SDK is installed. Note that the performance improvement is not due to improved coalescing in either case, but to avoiding redundant transfers from global memory. Where to Install Redistributed CUDA Libraries, 17.4. Furthermore, there should be multiple active blocks per multiprocessor so that blocks that arent waiting for a __syncthreads() can keep the hardware busy. Furthermore, the need for context switching can reduce utilization when work from several contexts could otherwise execute concurrently (see also Concurrent Kernel Execution). (BFloat16 only supports FP32 as accumulator), unsigned char/signed char (8-bit precision). Load the GPU program and execute, caching data on-chip for performance. Shared memory is allocated per thread block, so all threads in the block have access to the same shared memory. The compiler and hardware thread scheduler will schedule instructions as optimally as possible to avoid register memory bank conflicts. Asynchronous copy achieves better performance in nearly all cases. 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). These instructions also avoid using extra registers for memory copies and can also bypass the L1 cache. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. The effective bandwidth of this kernel is 140.2 GB/s on an NVIDIA Tesla V100.These results are lower than those obtained by the final kernel for C = AB. What is a word for the arcane equivalent of a monastery? However, once the size of this persistent data region exceeds the size of the L2 set-aside cache portion, approximately 10% performance drop is observed due to thrashing of L2 cache lines. Throughout this guide, specific recommendations are made regarding the design and implementation of CUDA C++ code. Any PTX device code loaded by an application at runtime is compiled further to binary code by the device driver. For GPUs with compute capability 8.6 maximum shared memory per thread block is 99 KB. When using a shared or static library, follow the release notes of said library to determine if the library supports minor version compatibility. At a minimum, you would need some sort of selection process that can access the heads of each queue. Context switches (when two threads are swapped) are therefore slow and expensive. 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. When we can, we should use registers. This document is not a commitment to develop, release, or deliver any Material (defined below), code, or functionality. On GPUs with GDDR memory with ECC enabled the available DRAM is reduced by 6.25% to allow for the storage of ECC bits. So while the impact is still evident it is not as large as we might have expected. 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. With wrap, x is replaced by frac(x) where frac(x) = x - floor(x). In other words, the term local in the name does not imply faster access. Prefer shared memory access where possible. The current GPU core temperature is reported, along with fan speeds for products with active cooling. 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. \left( 0.877 \times 10^{9} \right. 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 is the default if using nvcc to link in CUDA 5.5 and later. Applying Strong and Weak Scaling, 6.3.2. Shared memory is a powerful feature for writing well optimized CUDA code. In this section, we will review the usage patterns that may require new user workflows when taking advantage of the compatibility features of the CUDA platform. One or more compute capability versions can be specified to the nvcc compiler while building a file; compiling for the native compute capability for the target GPU(s) of the application is important to ensure that application kernels achieve the best possible performance and are able to use the features that are available on a given generation of GPU. One of several factors that determine occupancy is register availability. To maintain architectural compatibility, static shared memory allocations remain limited to 48 KB, and an explicit opt-in is also required to enable dynamic allocations above this limit. Support for Bfloat16 Tensor Core, through HMMA instructions. Because transfers should be minimized, programs that run multiple kernels on the same data should favor leaving the data on the device between kernel calls, rather than transferring intermediate results to the host and then sending them back to the device for subsequent calculations. The cudaEventElapsedTime() function returns the time elapsed between the recording of the start and stop events. 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(). An application has no direct control over these bank conflicts. However, the SONAME of this library is given as libcublas.so.5.5: Because of this, even if -lcublas (with no version number specified) is used when linking the application, the SONAME found at link time implies that libcublas.so.5.5 is the name of the file that the dynamic loader will look for when loading the application and therefore must be the name of the file (or a symlink to the same) that is redistributed with the application. A kernel to illustrate non-unit stride data copy. 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. For global memory accesses, this actual throughput is reported by the Global Load Throughput and Global Store Throughput values. 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). They produce equivalent results. This chapter discusses how to correctly measure performance using CPU timers and CUDA events. On Linux systems, the CUDA driver and kernel mode components are delivered together in the NVIDIA display driver package. The throughput of __sinf(x), __cosf(x), and__expf(x) is much greater than that of sinf(x), cosf(x), and expf(x). Please refer to the EULA for details. Both correctable single-bit and detectable double-bit errors are reported. Page-locked memory mapping is enabled by calling cudaSetDeviceFlags() with cudaDeviceMapHost. The only performance issue with shared memory is bank conflicts, which we will discuss later. This kernel has an effective bandwidth of 144.4 GB/s on an NVIDIA Tesla V100. 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. An example would be modeling how two molecules interact with each other, where the molecule sizes are fixed. Certain memory access patterns enable the hardware to coalesce groups of reads or writes of multiple data items into one operation. NVIDIA shall have no liability for the consequences or use of such information or for any infringement of patents or other rights of third parties that may result from its use. Latency hiding and occupancy depend on the number of active warps per multiprocessor, which is implicitly determined by the execution parameters along with resource (register and shared memory) constraints. 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. Note this switch is effective only on single-precision floating point. Therefore, it is best to avoid multiple contexts per GPU within the same CUDA application. However we now add the underlying driver to that mix. For more information please refer to the section on Async Copy in the CUDA C++ Programming Guide. The PTX string generated by NVRTC can be loaded by cuModuleLoadData and cuModuleLoadDataEx. To minimize bank conflicts, it is important to understand how memory addresses map to memory banks and how to optimally schedule memory requests. 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). 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. (Note that on devices of Compute Capability 1.2 or later, the memory system can fully coalesce even the reversed index stores to global memory. 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. 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. 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. Conditionally use features to remain compatible against older drivers. With each generation of NVIDIA processors, new features are added to the GPU that CUDA can leverage. For example, on devices of compute capability 7.0 each multiprocessor has 65,536 32-bit registers and can have a maximum of 2048 simultaneous threads resident (64 warps x 32 threads per warp). Computing a row of a tile in C using one row of A and an entire tile of B. Shared Memory. The larger N is(that is, the greater the number of processors), the smaller the P/N fraction. The following issues should be considered when determining what parts of an application to run on the device: The device is ideally suited for computations that can be run on numerous data elements simultaneously in parallel. 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. As seen above, in the case of misaligned sequential accesses, caches help to alleviate the performance impact. See Math Libraries. This value is expressed in milliseconds and has a resolution of approximately half a microsecond. The results are shown in the chart below, where we see good performance regardless of whether the persistent data fits in the L2 set-aside or not. 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 is an aggressive optimization that can both reduce numerical accuracy and alter special case handling. 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. 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. Other company and product names may be trademarks of the respective companies with which they are associated. The reason shared memory is used in this example is to facilitate global memory coalescing on older CUDA devices (Compute Capability 1.1 or earlier). We evaluate the performance of both kernels using elements of size 4B, 8B and 16B per thread i.e., using int, int2 and int4 for the template parameter. 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. CUDA Toolkit is released on a monthly release cadence to deliver new features, performance improvements, and critical bug fixes. Data that cannot be laid out so as to enable coalescing, or that doesnt have enough locality to use the L1 or texture caches effectively, will tend to see lesser speedups when used in computations on GPUs. Unified memory: supports seamless access to buffers or objects from multiple GPUs and CPUs. If you want to communicate (i.e. CUDA Compatibility Developers Guide, 15.3.1. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. This information is obtained by calling cudaGetDeviceProperties() and accessing the information in the structure it returns. The third generation NVLink has the same bi-directional data rate of 50 GB/s per link, but uses half the number of signal pairs to achieve this bandwidth.