cuda shared memory between blocks

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. 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. For example, cuMemMap APIs or any of APIs introduced prior to CUDA 11.0, such as cudaDeviceSynchronize, do not require a driver upgrade. Finally, higher bandwidth between the host and the device is achieved when using page-locked (or pinned) memory, as discussed in the CUDA C++ Programming Guide and the Pinned Memory section of this document. This feature enables CUDA kernels to overlap copying data from global to shared memory with computation. Incorrect or unexpected results arise principally from issues of floating-point accuracy due to the way floating-point values are computed and stored. NVIDIA Ampere GPU Architecture Tuning, 1.4.1.2. As a result, it is recommended that first-time readers proceed through the guide sequentially. 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. The performance of the kernels is shown in Figure 14. The PTX string generated by NVRTC can be loaded by cuModuleLoadData and cuModuleLoadDataEx. Asking for help, clarification, or responding to other answers. In particular, a larger block size does not imply a higher occupancy. But this technique is still useful for other access patterns, as Ill show in the next post.). The for loop over i multiplies a row of A by a column of B, which is then written to C. The effective bandwidth of this kernel is 119.9 GB/s on an NVIDIA Tesla V100. 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. Mapping Persistent data accesses to set-aside L2 in sliding window experiment. For other algorithms, implementations may be considered correct if they match the reference within some small epsilon. Kernels can be written using the CUDA instruction set architecture, called PTX, which is described in the PTX reference manual. NVIDIA makes no representation or warranty that products based on this document will be suitable for any specified use. Consequently, its important to understand the characteristics of the architecture. They can be distinguished by their names: some have names with prepended underscores, whereas others do not (e.g., __functionName() versus functionName()). CUDA shared memory writes incur unexplainable long latency, CUDA atomic function usage with volatile shared memory. Note that cudaSetDeviceFlags() must be called prior to setting a device or making a CUDA call that requires state (that is, essentially, before a context is created). 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. Parallelizing these functions as well should increase our speedup potential. Block-column matrix multiplied by block-row matrix. The details of managing the accelerator device are handled implicitly by an OpenACC-enabled compiler and runtime. Computing a row of a tile in C using one row of A and an entire tile of B. Using asynchronous copies does not use any intermediate register. 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. Using Kolmogorov complexity to measure difficulty of problems? On discrete GPUs, mapped pinned memory is advantageous only in certain cases. 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. Having identified the hotspots and having done the basic exercises to set goals and expectations, the developer needs to parallelize the code. Thread instructions are executed sequentially in CUDA, and, as a result, executing other warps when one warp is paused or stalled is the only way to hide latencies and keep the hardware busy. Because the driver may interleave execution of CUDA calls from other non-default streams, calls in other streams may be included in the timing. In the NVIDIA Ampere GPU architecture, the portion of the L1 cache dedicated to shared memory (known as the carveout) can be selected at runtime as in previous architectures such as Volta, using cudaFuncSetAttribute() with the attribute cudaFuncAttributePreferredSharedMemoryCarveout. The primary differences are in threading model and in separate physical memories: Execution pipelines on host systems can support a limited number of concurrent threads. 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. As described in Asynchronous and Overlapping Transfers with Computation, CUDA streams can be used to overlap kernel execution with data transfers. There are many such factors involved in selecting block size, and inevitably some experimentation is required. Both of your questions imply some sort of global synchronization. Block-column matrix (A) multiplied by block-row matrix (B) with resulting product matrix (C).. An application can also use the Occupancy API from the CUDA Runtime, e.g. It is important to include the overhead of transferring data to and from the device in determining whether operations should be performed on the host or on the device. 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. How to notate a grace note at the start of a bar with lilypond? See the nvidia-smi documenation for details. 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. Two types of runtime math operations are supported. 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. To understand the performance difference between synchronous copy and asynchronous copy of data from global memory to shared memory, consider the following micro benchmark CUDA kernels for demonstrating the synchronous and asynchronous approaches. An explicit __syncwarp() can be used to guarantee that the warp has reconverged for subsequent instructions. Zero copy can be used in place of streams because kernel-originated data transfers automatically overlap kernel execution without the overhead of setting up and determining the optimal number of streams. CUDA compatibility allows users to update the latest CUDA Toolkit software (including the compiler, libraries, and tools) without requiring update to the entire driver stack. Resources stay allocated to each thread until it completes its execution. A trivial example is when the controlling condition depends only on (threadIdx / WSIZE) where WSIZE is the warp size. The async-copy does not require the copy_count parameter to be a multiple of 4, to maximize performance through compiler optimizations. Functions following the __functionName() naming convention map directly to the hardware level. Also, because of the overhead associated with each transfer, batching many small transfers into one larger transfer performs significantly better than making each transfer separately, even if doing so requires packing non-contiguous regions of memory into a contiguous buffer and then unpacking after the transfer. 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. By exposing parallelism to the compiler, directives allow the compiler to do the detailed work of mapping the computation onto the parallel architecture. 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). Shared Memory in Matrix Multiplication (C=AAT), 9.2.3.4. The operating system must swap threads on and off CPU execution channels to provide multithreading capability. On Linux systems, the CUDA driver and kernel mode components are delivered together in the NVIDIA display driver package. The NVIDIA Ampere GPU architecture is NVIDIAs latest architecture for CUDA compute applications. Error counts are provided for both the current boot cycle and the lifetime of the GPU. Not using intermediate registers can help reduce register pressure and can increase kernel occupancy. However, striding through global memory is problematic regardless of the generation of the CUDA hardware, and would seem to be unavoidable in many cases, such as when accessing elements in a multidimensional array along the second and higher dimensions. The same goes for other CUDA Toolkit libraries: cuFFT has an interface similar to that of FFTW, etc. 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. Consequently, the order in which arithmetic operations are performed is important. Managing your GPU cluster will help achieve maximum GPU utilization and help you and your users extract the best possible performance. 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. The CUDA Toolkits End-User License Agreement (EULA) allows for redistribution of many of the CUDA libraries under certain terms and conditions. cudart 11.1 is statically linked) is run on the system, we see that it runs successfully even when the driver reports a 11.0 version - that is, without requiring the driver or other toolkit components to be updated on the system. There are two options: clamp and wrap. For 32-bit applications, the file would be cublas32_55.dll. CUDA driver - User-mode driver component used to run CUDA applications (e.g. What if you need multiple dynamically sized arrays in a single kernel? Whats the grammar of "For those whose stories they are"? When using a shared or static library, follow the release notes of said library to determine if the library supports minor version compatibility. Not all threads need to participate. This access pattern results in four 32-byte transactions, indicated by the red rectangles. Current utilization rates are reported for both the compute resources of the GPU and the memory interface. Setting the bank size to eight bytes can help avoid shared memory bank conflicts when accessing double precision data. Threads on a CPU are generally heavyweight entities. 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. Before we proceed further on this topic, its important for developers to understand the concept of Minimum Driver Version and how that may affect them. .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. When the latter is much lower than the former, design or implementation details are likely to reduce bandwidth, and it should be the primary goal of subsequent optimization efforts to increase it. APOD is a cyclical process: initial speedups can be achieved, tested, and deployed with only minimal initial investment of time, at which point the cycle can begin again by identifying further optimization opportunities, seeing additional speedups, and then deploying the even faster versions of the application into production. Devices of compute capability 3.x allow a third setting of 32KB shared memory / 32KB L1 cache which can be obtained using the optioncudaFuncCachePreferEqual. 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). If you want to communicate (i.e. 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. 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 repeated reading of the B tile can be eliminated by reading it into shared memory once (Improvement by reading additional data into shared memory). 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. If the shared memory array size is known at compile time, as in the staticReverse kernel, then we can explicitly declare an array of that size, as we do with the array s. In this kernel, t and tr are the two indices representing the original and reverse order, respectively. The easiest option is to statically link against the CUDA Runtime. See Building for Maximum Compatibility for further discussion of the flags used for building code for multiple generations of CUDA-capable device simultaneously. 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. Armed with this knowledge, the developer can evaluate these bottlenecks for parallelization and start to investigate GPU acceleration. An upgraded driver matching the CUDA runtime version is currently required for those APIs. See Hardware Multithreading of the CUDA C++ Programming Guide for the register allocation formulas for devices of various compute capabilities and Features and Technical Specifications of the CUDA C++ Programming Guide for the total number of registers available on those devices. However, for each iteration i, all threads in a warp read the same value from global memory for matrix A, as the index row*TILE_DIM+i is constant within a warp. 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. Register pressure occurs when there are not enough registers available for a given task. The NVIDIA Ampere GPU architectures Streaming Multiprocessor (SM) provides the following improvements over Volta and Turing. The host system and the device each have their own distinct attached physical memories 1. The goal is to maximize the use of the hardware by maximizing bandwidth. Because the data is not cached on the GPU, mapped pinned memory should be read or written only once, and the global loads and stores that read and write the memory should be coalesced. An example is transposing [1209, 9] of any type and 32 tile size. 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. When our CUDA 11.1 application (i.e. The right value for minBlocksPerMultiprocessor should be determined using a detailed per kernel analysis. What is the difference between CUDA shared memory and global - Quora The high-priority recommendations from those guides are as follows: Find ways to parallelize sequential code. In such cases, call cudaGetDeviceProperties() to determine whether the device is capable of a certain feature. High Priority: Ensure global memory accesses are coalesced whenever possible. This kernel has an effective bandwidth of 144.4 GB/s on an NVIDIA Tesla V100. The first segment shows the reference sequential implementation, which transfers and operates on an array of N floats (where N is assumed to be evenly divisible by nThreads). See Math Libraries. In CUDA only threads and the host can access memory. The throughput of individual arithmetic operations is detailed in the CUDA C++ Programming Guide. This new feature is exposed via the pipeline API in CUDA. The following sections discuss some caveats and considerations. However, bank conflicts occur when copying the tile from global memory into shared memory. This is done by carefully choosing the execution configuration of each kernel launch. A system with multiple GPUs may contain GPUs of different hardware versions and capabilities. Unified Shared Memory/L1/Texture Cache, NVIDIA Ampere GPU Architecture Compatibility Guide for CUDA Applications. 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. The number of elements is multiplied by the size of each element (4 bytes for a float), multiplied by 2 (because of the read and write), divided by 109 (or 1,0243) to obtain GB of memory transferred. 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. 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/. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. 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. NVIDIA Ampere GPU Architecture Tuning Guide 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. NVIDIA accepts no liability for inclusion and/or use of NVIDIA products in such equipment or applications and therefore such inclusion and/or use is at customers own risk. Some calculations use 10243 instead of 109 for the final calculation. Bandwidth is best served by using as much fast memory and as little slow-access memory as possible. Furthermore, there should be multiple active blocks per multiprocessor so that blocks that arent waiting for a __syncthreads() can keep the hardware busy. Package managers facilitate this process but unexpected issues can still arise and if a bug is found, it necessitates a repeat of the above upgrade process. If the GPU must wait on one warp of threads, it simply begins executing work on another. Recommendations for building a minor-version compatible library, 15.4.1.5. After the application is dynamically linked against the CUDA Runtime, this version of the runtime library should be bundled with the application. All rights reserved. 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 cubins are architecture-specific. See the CUDA C++ Programming Guide for details. The compiler must on occasion insert conversion instructions, introducing additional execution cycles. However, this approach of determining how register count affects occupancy does not take into account the register allocation granularity. 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. A grid of N/w by M/w blocks is launched, where each thread block calculates the elements of a different tile in C from a single tile of A and a single tile of B. Block-column matrix multiplied by block-row matrix. From CUDA 11.3 NVRTC is also semantically versioned. 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. Page-locked memory mapping is enabled by calling cudaSetDeviceFlags() with cudaDeviceMapHost. CUDA Memory Global Memory We used global memory to hold the functions values. Devices of compute capability 1.3 and higher provide native support for double-precision floating-point values (that is, values 64 bits wide). Global memory: is the memory residing graphics/accelerator card but not inside GPU chip. All threads within one block see the same shared memory array . PDF L15: CUDA, cont. Memory Hierarchy and Examples The performance of the sliding-window benchmark with tuned hit-ratio. 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. Is a PhD visitor considered as a visiting scholar? Similarly, the single-precision functions sinpif(), cospif(), and sincospif() should replace calls to sinf(), cosf(), and sincosf() when the function argument is of the form *. Failure to do so could lead to too many resources requested for launch errors. A CUDA context is a software environment that manages memory and other resources In particular, there is no register-related reason to pack data into vector data types such as float4 or int4 types. The core computational unit, which includes control, arithmetic, registers and typically some cache, is replicated some number of times and connected to memory via a network. In order to maintain binary compatibility across minor versions, the CUDA runtime no longer bumps up the minimum driver version required for every minor release - this only happens when a major release is shipped. 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). This can be configured during runtime API from the host for all kernelsusing cudaDeviceSetCacheConfig() or on a per-kernel basis using cudaFuncSetCacheConfig(). The NVIDIA A100 GPU based on compute capability 8.0 increases the maximum capacity of the combined L1 cache, texture cache and shared memory to 192 KB, 50% larger than the L1 cache in NVIDIA V100 GPU. These copy instructions are asynchronous, with respect to computation and allow users to explicitly control overlap of compute with data movement from global memory into the SM. Other company and product names may be trademarks of the respective companies with which they are associated. 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 read-only texture memory space is cached. 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. 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. 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 GPUs with compute capability 8.6, shared memory capacity per SM is 100 KB. 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. A natural decomposition of the problem is to use a block and tile size of wxw threads. In order to maintain forward compatibility to future hardware and toolkits and to ensure that at least one thread block can run on an SM, developers should include the single argument __launch_bounds__(maxThreadsPerBlock) which specifies the largest block size that the kernel will be launched with.