cuda shared memory between blocks cuda shared memory between blocks

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. Devices of compute capability 2.0 and higher have the additional ability to multicast shared memory accesses, meaning that multiple accesses to the same location by any number of threads within a warp are served simultaneously. Various dynamic and static information is reported, including board serial numbers, PCI device IDs, VBIOS/Inforom version numbers and product names. Providing the two argument version of __launch_bounds__(maxThreadsPerBlock,minBlocksPerMultiprocessor) can improve performance in some cases. When sharing data between threads, we need to be careful to avoid race conditions, because while threads in a block run logically in parallel, not all threads can execute physically at the same time. The criteria of benefit and scope for establishing priority will vary depending on the nature of the program. Warp level support for Reduction Operations, 1.4.2.1. The cause of the difference is shared memory bank conflicts. In Unoptimized handling of strided accesses to global memory, the row-th, col-th element of C is obtained by taking the dot product of the row-th and col-th rows of A. Instead, all instructions are scheduled, but a per-thread condition code or predicate controls which threads execute the instructions. Other company and product names may be trademarks of the respective companies with which they are associated. High Priority: To maximize developer productivity, profile the application to determine hotspots and bottlenecks. CUDA C++ provides a simple path for users familiar with the C++ programming language to easily write programs for execution by the device. The latency of most arithmetic instructions is typically 4 cycles on devices of compute capability 7.0. Furthermore, if accesses by the threads of the warp had been permuted within or accross the four segments, still only four 32-byte transactions would have been performed by a device with compute capability 6.0 or higher. 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. Shared memory accesses, in counterpoint, are usually worth optimizing only when there exists a high degree of bank conflicts. This Best Practices Guide is a manual to help developers obtain the best performance from NVIDIA CUDA GPUs. 11.x). Local memory is used only to hold automatic variables. By using new CUDA versions, users can benefit from new CUDA programming model APIs, compiler optimizations and math library features. This is because the user could only allocate the CUDA static shared memory up to 48 KB. There are a number of tools that can be used to generate the profile. There is no way to check this for a specific variable, but the compiler reports total local memory usage per kernel (lmem) when run with the--ptxas-options=-v option. In other words, the term local in the name does not imply faster access. More details about the compute capabilities of various GPUs are in CUDA-Enabled GPUs and Compute Capabilities of the CUDA C++ Programming Guide. 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. In many applications, a combination of strong and weak scaling is desirable. 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, if the install name of the cuBLAS library is given as @rpath/libcublas.5.5.dylib, then the library is version 5.5 and the copy of this library redistributed with the application must be named libcublas.5.5.dylib, even though only -lcublas (with no version number specified) is used at link time. 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. Not the answer you're looking for? Because of this, the maximum speedup S of a program is: Another way of looking at Gustafsons Law is that it is not the problem size that remains constant as we scale up the system but rather the execution time. 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. 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. Asynchronous copies are hardware accelerated for NVIDIA A100 GPU. They are faster but provide somewhat lower accuracy (e.g., __sinf(x) and __expf(x)). 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. Each floating-point arithmetic operation involves a certain amount of rounding. Increased L2 capacity and L2 Residency Controls, 1.4.2.3. It would have been more so if adjacent warps had not exhibited such a high degree of reuse of the over-fetched cache lines. Using asynchronous copies does not use any intermediate register. For some architectures L1 and shared memory use same hardware and are configurable. Instructions with a false predicate do not write results, and they also do not evaluate addresses or read operands. Therefore, a texture fetch costs one device memory read only on a cache miss; otherwise, it just costs one read from the texture cache. The application will then enumerate these devices as device 0 and device 1, respectively. Instead, each such instruction is associated with a per-thread condition code or predicate that is set to true or false according to the controlling condition. As a result, it is recommended that first-time readers proceed through the guide sequentially. Then, as shown in the figure below, we specify that the accesses to the first freqSize * sizeof(int) bytes of the memory region are persistent. 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. These recommendations are categorized by priority, which is a blend of the effect of the recommendation and its scope. Applications composed with these differences in mind can treat the host and device together as a cohesive heterogeneous system wherein each processing unit is leveraged to do the kind of work it does best: sequential work on the host and parallel work on the device. Effective bandwidth is calculated by timing specific program activities and by knowing how data is accessed by the program. The compiler optimizes 1.0f/sqrtf(x) into rsqrtf() only when this does not violate IEEE-754 semantics. For example, Overlapping computation and data transfers demonstrates how host computation in the routine cpuFunction() is performed while data is transferred to the device and a kernel using the device is executed. By understanding how applications can scale it is possible to set expectations and plan an incremental parallelization strategy. 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. On devices of compute capability 6.0 or higher, L1-caching is the default, however the data access unit is 32-byte regardless of whether global loads are cached in L1 or not. Other peculiarities of floating-point arithmetic are presented in Features and Technical Specifications of the CUDA C++ Programming Guide as well as in a whitepaper and accompanying webinar on floating-point precision and performance available from https://developer.nvidia.com/content/precision-performance-floating-point-and-ieee-754-compliance-nvidia-gpus. //Such that up to 20MB of data is resident. Overall, developers can expect similar occupancy as on Volta without changes to their application. A CUDA context is a software environment that manages memory and other resources 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. In reality, most applications do not exhibit perfectly linear strong scaling, even if they do exhibit some degree of strong scaling. 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. 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. 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). The performance of the sliding-window benchmark with fixed hit-ratio of 1.0. Some aspects of this behavior such as cache location and maximum cache size can be controlled via the use of environment variables; see Just in Time Compilation of the CUDA C++ Programming Guide. On integrated GPUs (i.e., GPUs with the integrated field of the CUDA device properties structure set to 1), mapped pinned memory is always a performance gain because it avoids superfluous copies as integrated GPU and CPU memory are physically the same. 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. For more information on the Arrive/Wait Barriers refer to the Arrive/Wait Barrier section in the CUDA C++ Programming Guide. Other differences are discussed as they arise elsewhere in this document. For portability, that is, to be able to execute code on future GPU architectures with higher compute capability (for which no binary code can be generated yet), an application must load PTX code that will be just-in-time compiled by the NVIDIA driver for these future devices. Devices of compute capability 3.x allow a third setting of 32KB shared memory / 32KB L1 cache which can be obtained using the optioncudaFuncCachePreferEqual. The CUDA Driver API thus is binary-compatible (the OS loader can pick up a newer version and the application continues to work) but not source-compatible (rebuilding your application against a newer SDK might require source changes). 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. How to manage this resource utilization is discussed in the final sections of this chapter. Likewise, for exponentation with an exponent of -1/3, use rcbrt() or rcbrtf(). 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. 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. The NVIDIA Ampere GPU architecture includes new Third Generation Tensor Cores that are more powerful than the Tensor Cores used in Volta and Turing SMs. Page-locked memory mapping is enabled by calling cudaSetDeviceFlags() with cudaDeviceMapHost. For each iteration i of the for loop, the threads in a warp read a row of the B tile, which is a sequential and coalesced access for all compute capabilities. Whether a device has this capability is indicated by the concurrentKernels field of the cudaDeviceProp structure (or listed in the output of the deviceQuery CUDA Sample). 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. 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. 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 only performance issue with shared memory is bank conflicts, which we will discuss later. Note this switch is effective only on single-precision floating point. In particular, developers should note the number of multiprocessors on the device, the number of registers and the amount of memory available, and any special capabilities of the device. Use several smaller thread blocks rather than one large thread block per multiprocessor if latency affects performance. Dynamic parallelism - passing contents of shared memory to spawned blocks? To keep the kernels simple, M and N are multiples of 32, since the warp size (w) is 32 for current devices. 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). Kernel access to global memory also should be minimized by maximizing the use of shared memory on the device. Please refer to the EULA for details. In the example above, we can clearly see that the function genTimeStep() takes one-third of the total running time of the application. The throughput of __sinf(x), __cosf(x), and__expf(x) is much greater than that of sinf(x), cosf(x), and expf(x). 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). exchange data) between threadblocks, the only method is to use global memory. They produce equivalent results. This limitation is not specific to CUDA, but an inherent part of parallel computation on floating-point values. When using branch predication, none of the instructions whose execution depends on the controlling condition is skipped. Is it possible to create a concave light? Because execution within a stream occurs sequentially, none of the kernels will launch until the data transfers in their respective streams complete. 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. A key concept in this effort is occupancy, which is explained in the following sections. Increment major versions when there are ABI breaking changes such as API deprecation and modifications. 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). For example, the NVIDIA Tesla V100 uses HBM2 (double data rate) RAM with a memory clock rate of 877 MHz and a 4096-bit-wide memory interface. (Factorization). By exposing parallelism to the compiler, directives allow the compiler to do the detailed work of mapping the computation onto the parallel architecture. Computing a row of a tile. Not requiring driver updates for new CUDA releases can mean that new versions of the software can be made available faster to users. For more details on the new Tensor Core operations refer to the Warp Matrix Multiply section in the CUDA C++ Programming Guide. Minimize redundant accesses to global memory whenever possible. Compiler JIT Cache Management Tools, 18.1. The NVIDIA A100 GPU increases the HBM2 memory capacity from 32 GB in V100 GPU to 40 GB in A100 GPU. 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. The cudaChooseDevice() function can be used to select the device that most closely matches a desired set of features. Asynchronous copy achieves better performance in nearly all cases. 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. 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. 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. 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. Although each of these instructions is scheduled for execution, only the instructions with a true predicate are actually executed. Both pow() and powf() are heavy-weight functions in terms of register pressure and instruction count due to the numerous special cases arising in general exponentiation and the difficulty of achieving good accuracy across the entire ranges of the base and the exponent. These exceptions, which are detailed in Features and Technical Specifications of the CUDA C++ Programming Guide, can lead to results that differ from IEEE 754 values computed on the host system. 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. 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. To target specific versions of NVIDIA hardware and CUDA software, use the -arch, -code, and -gencode options of nvcc. Two types of runtime math operations are supported. NVIDIA Ampere GPU Architecture Tuning, 1.4.1.2. 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. These are the primary hardware differences between CPU hosts and GPU devices with respect to parallel programming. On parallel systems, it is possible to run into difficulties not typically found in traditional serial-oriented programming. 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. Access to shared memory is much faster than global memory access because it is located on chip. The L2 cache set-aside size for persisting accesses may be adjusted, within limits: Mapping of user data to L2 set-aside portion can be controlled using an access policy window on a CUDA stream or CUDA graph kernel node. Applications compiled with CUDA toolkit versions as old as 3.2 will run on newer drivers. Global, local, and texture memory have the greatest access latency, followed by constant memory, shared memory, and the register file. Figure 6 illustrates such a situation; in this case, threads within a warp access words in memory with a stride of 2. Higher occupancy does not always equate to higher performance-there is a point above which additional occupancy does not improve performance. Shared memory is extremely fast, user managed, on-chip memory that can be used to share data between threads within a thread block. Overlapping computation and data transfers. If the GPU must wait on one warp of threads, it simply begins executing work on another. By comparison, threads on GPUs are extremely lightweight. 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. As even CPU architectures will require exposing parallelism in order to improve or simply maintain the performance of sequential applications, the CUDA family of parallel programming languages (CUDA C++, CUDA Fortran, etc.) Single-precision floats provide the best performance, and their use is highly encouraged. 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. Assess, Parallelize, Optimize, Deploy, 3.1.3.1. The operating system must swap threads on and off CPU execution channels to provide multithreading capability. The results of these optimizations are summarized in Table 3. A Sequential but Misaligned Access Pattern, 9.2.2.2. Devices of compute capability 2.0 and later support a special addressing mode called Unified Virtual Addressing (UVA) on 64-bit Linux and Windows. Tuning CUDA Applications for NVIDIA Ampere GPU Architecture. CUDA Compatibility Developers Guide, 15.3.1. 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). When JIT compilation of PTX device code is used, the NVIDIA driver caches the resulting binary code on disk. Choosing execution parameters is a matter of striking a balance between latency hiding (occupancy) and resource utilization. Medium Priority: Use the fast math library whenever speed trumps precision. Declare shared memory in CUDA C/C++ device code using the__shared__variable declaration specifier. Devices of compute capability 3.x have configurable bank size, which can be set using cudaDeviceSetSharedMemConfig() to either four bytes (cudaSharedMemBankSizeFourByte, thedefault) or eight bytes (cudaSharedMemBankSizeEightByte). The larger N is(that is, the greater the number of processors), the smaller the P/N fraction. Starting with the Volta architecture, Independent Thread Scheduling allows a warp to remain diverged outside of the data-dependent conditional block. If the PTX is also not available, then the kernel launch will fail. 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. 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. The following example illustrates the basic technique. To view a librarys install name, use the otool -L command: The binary compatibility version of the CUDA libraries on Windows is indicated as part of the filename. See the CUDA C++ Programming Guide for details. 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. In the NVIDIA Ampere GPU architecture remote NVLINK accesses go through a Link TLB on the remote GPU. Developers are notified through deprecation and documentation mechanisms of any current or upcoming changes. Lets assume that A and B are threads in two different warps. This chapter contains a summary of the recommendations for optimization that are explained in this document. For devices of compute capability 1.x, the warp size is 32 threads and the number of banks is 16. Having a semantically versioned ABI means the interfaces need to be maintained and versioned. Optimizations can be applied at various levels, from overlapping data transfers with computation all the way down to fine-tuning floating-point operation sequences. For other algorithms, implementations may be considered correct if they match the reference within some small epsilon. Mapping Persistent data accesses to set-aside L2 in sliding window experiment. To do this, the simpleMultiply kernel (Unoptimized matrix multiplication) calculates the output elements of a tile of matrix C. In Unoptimized matrix multiplication, a, b, and c are pointers to global memory for the matrices A, B, and C, respectively; blockDim.x, blockDim.y, and TILE_DIM are all equal to w. Each thread in the wxw-thread block calculates one element in a tile of C. row and col are the row and column of the element in C being calculated by a particular thread. It allows developers to use a CUDA-enabled graphics processing unit (GPU) to accelerate processing tasks in their applications. No license, either expressed or implied, is granted under any NVIDIA patent right, copyright, or other NVIDIA intellectual property right under this document. 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. Whether a device has this capability is indicated by the asyncEngineCount field of the cudaDeviceProp structure (or listed in the output of the deviceQuery CUDA Sample). The versions of the components in the toolkit are available in this table. 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. This is common for building applications that are GPU architecture, platform and compiler agnostic. So, if each thread block uses many registers, the number of thread blocks that can be resident on a multiprocessor is reduced, thereby lowering the occupancy of the multiprocessor. The NVIDIA A100 GPU supports shared memory capacity of 0, 8, 16, 32, 64, 100, 132 or 164 KB per SM. As you have correctly said, if only one block fits per SM because of the amount of shared memory used, only one block will be scheduled at any one time. As a result, all modern processors require parallel code in order to achieve good utilization of their computational power. The results of the various optimizations are summarized in Table 2. High Priority: Ensure global memory accesses are coalesced whenever possible. 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). This variant simply uses the transpose of A in place of B, so C = AAT. cudaDeviceSynchronize()blocks the calling CPU thread until all CUDA calls previously issued by the thread are completed. (For further information, refer to Performance Guidelines in the CUDA C++ Programming Guide). ? vegan) just to try it, does this inconvenience the caterers and staff? Along with the increased capacity, the bandwidth of the L2 cache to the SMs is also increased. By reversing the array using shared memory we are able to have all global memory reads and writes performed with unit stride, achieving full coalescing on any CUDA GPU. 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. 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. FP16 / FP32 But since any repeated access to such memory areas causes repeated CPU-GPU transfers, consider creating a second area in device memory to manually cache the previously read host memory data. Making statements based on opinion; back them up with references or personal experience. Testing of all parameters of each product is not necessarily performed by NVIDIA. This is because some operations common to each element can be performed by the thread once, amortizing the cost over the number of shared memory elements processed by the thread. Using unrealistic workloads can lead to sub-optimal results and wasted effort both by causing developers to optimize for unrealistic problem sizes and by causing developers to concentrate on the wrong functions. The CUDA software environment consists of three parts: CUDA Toolkit (libraries, CUDA runtime and developer tools) - SDK for developers to build CUDA applications.