cuda shared memory between blocks

Assess, Parallelize, Optimize, Deploy, 3.1.3.1. While the contents can be used as a reference manual, you should be aware that some topics are revisited in different contexts as various programming and configuration topics are explored. The application should also maximize parallel execution at a higher level by explicitly exposing concurrent execution on the device through streams, as well as maximizing concurrent execution between the host and the device. -use_fast_math compiler option of nvcc coerces every functionName() call to the equivalent __functionName() call. 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. CUDA: Explainer of a kernel with 2D blocks, shared memory, atomics Last updated on Feb 27, 2023. cudaFuncAttributePreferredSharedMemoryCarveout, 1. Global memory loads and stores by threads of a warp are coalesced by the device into as few as possible transactions. It is customers sole responsibility to evaluate and determine the applicability of any information contained in this document, ensure the product is suitable and fit for the application planned by customer, and perform the necessary testing for the application in order to avoid a default of the application or the product. Increment major versions when there are ABI breaking changes such as API deprecation and modifications. An application can also use the Occupancy API from the CUDA Runtime, e.g. No contractual obligations are formed either directly or indirectly by this document. In calculating each of the rows of a tile of matrix C, the entire tile of B is read. More information on cubins, PTX and application compatibility can be found in the CUDA C++ Programming Guide. An upgraded driver matching the CUDA runtime version is currently required for those APIs. Please note that new versions of nvidia-smi are not guaranteed to be backward-compatible with previous versions. There are several key strategies for parallelizing sequential code. Bandwidth is best served by using as much fast memory and as little slow-access memory as possible. Be aware that CPU-to-GPU synchronization points such as those mentioned in this section imply a stall in the GPUs processing pipeline and should thus be used sparingly to minimize their performance impact. These bindings expose the same features as the C-based interface and also provide backwards compatibility. The dynamic shared memory kernel, dynamicReverse(), declares the shared memory array using an unsized extern array syntax, extern shared int s[] (note the empty brackets and use of the extern specifier). Indicate whether the NVIDIA driver stays loaded when no applications are connected to the GPU. Data Transfer Between Host and Device, 9.1.2. Computing a row of a tile. Weaknesses in customers product designs may affect the quality and reliability of the NVIDIA product and may result in additional or different conditions and/or requirements beyond those contained in this document. For this reason, ensuring that as much as possible of the data in each cache line fetched is actually used is an important part of performance optimization of memory accesses on these devices. The number of blocks in a grid should be larger than the number of multiprocessors so that all multiprocessors have at least one block to execute. Misaligned sequential addresses that fall within five 32-byte segments, Memory allocated through the CUDA Runtime API, such as via cudaMalloc(), is guaranteed to be aligned to at least 256 bytes. As even CPU architectures require exposing this 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.) 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. 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). 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. Just-in-time compilation increases application load time but allows applications to benefit from latest compiler improvements. This is done with the FLDCW x86 assembly instruction or the equivalent operating system API. A stream is simply a sequence of operations that are performed in order on the device. NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any time without notice. 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. Bfloat16 provides 8-bit exponent i.e., same range as FP32, 7-bit mantissa and 1 sign-bit. Once the parallelism of the algorithm has been exposed, it needs to be mapped to the hardware as efficiently as possible. The amount of performance benefit an application will realize by running on CUDA depends entirely on the extent to which it can be parallelized. 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. Whats the grammar of "For those whose stories they are"? 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. 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 results of these optimizations are summarized in Table 3. Another common approach to parallelization of sequential codes is to make use of parallelizing compilers. Each floating-point arithmetic operation involves a certain amount of rounding. If no new features are used (or if they are used conditionally with fallbacks provided) youll be able to remain compatible. Because shared memory is shared by threads in a thread block, it provides a mechanism for threads to cooperate. Weak Scaling and Gustafsons Law describes weak scaling, where the speedup is attained by growing the problem size. The cudaDeviceEnablePeerAccess() API call remains necessary to enable direct transfers (over either PCIe or NVLink) between GPUs. For an existing project, the first step is to assess the application to locate the parts of the code that are responsible for the bulk of the execution time. When choosing the block size, it is important to remember that multiple concurrent blocks can reside on a multiprocessor, so occupancy is not determined by block size alone. As a particular example, to evaluate the sine function in degrees instead of radians, use sinpi(x/180.0). For purposes of calculating occupancy, the number of registers used by each thread is one of the key factors. This approach is most straightforward when the majority of the total running time of our application is spent in a few relatively isolated portions of the code. All of these products (nvidia-smi, NVML, and the NVML language bindings) are updated with each new CUDA release and provide roughly the same functionality. PTX defines a virtual machine and ISA for general purpose parallel thread execution. . For example, the ability to overlap kernel execution with asynchronous data transfers between the host and the device is available on most but not all GPUs irrespective of the compute capability. All CUDA threads can access it for read and write. The bandwidthTest CUDA Sample shows how to use these functions as well as how to measure memory transfer performance. CUDA work occurs within a process space for a particular GPU known as a context. CUDA 11.0 introduces an async-copy feature that can be used within device code to explicitly manage the asynchronous copying of data from global memory to shared memory. High Priority: Minimize the use of global memory. Instead, strategies can be applied incrementally as they are learned. A lower occupancy kernel will have more registers available per thread than a higher occupancy kernel, which may result in less register spilling to local memory; in particular, with a high degree of exposed instruction-level parallelism (ILP) it is, in some cases, possible to fully cover latency with a low occupancy. To ensure correct results when parallel threads cooperate, we must synchronize the threads. likewise return their own sets of error codes. This approach permits some overlapping of the data transfer and execution. 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. The cubins are architecture-specific. The easiest option is to statically link against the CUDA Runtime. You must declare a single extern unsized array as before, and use pointers into it to divide it into multiple arrays, as in the following excerpt. In this code, two streams are created and used in the data transfer and kernel executions as specified in the last arguments of the cudaMemcpyAsync call and the kernels execution configuration. A copy kernel that illustrates misaligned accesses. Strong Scaling and Amdahls Law describes strong scaling, which allows us to set an upper bound for the speedup with a fixed problem size. With the use of shared memory we can fetch data from global memory and place it into on-chip memory with far lower latency and higher bandwidth then global memory. 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. 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). Block-column matrix (A) multiplied by block-row matrix (B) with resulting product matrix (C).. As a result, a read from constant memory costs one memory read from device memory only on a cache miss; otherwise, it just costs one read from the constant cache. BFloat16 format is especially effective for DL training scenarios. Recovering from a blunder I made while emailing a professor. On Wednesday, February 19, 2020, NVIDIA will present part 2 of a 9-part CUDA Training Series titled "CUDA Shared Memory". A natural decomposition of the problem is to use a block and tile size of wxw threads. . NVIDIA Ampere GPU Architecture Tuning Guide, 1.4. Using asynchronous copies does not use any intermediate register. For other applications, the problem size will grow to fill the available processors. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. What is the difference between CUDA shared memory and global - Quora In both cases, kernels must be compiled into binary code by nvcc (called cubins) to execute on the device. Note that the timings are measured on the GPU clock, so the timing resolution is operating-system-independent. Furthermore, register allocations are rounded up to the nearest 256 registers per warp. 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. Furthermore, this file should be installed into the @rpath of the application; see Where to Install Redistributed CUDA Libraries. 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. 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. Dealing with relocatable objects is not yet supported, therefore the cuLink* set of APIs in the CUDA driver will not work with enhanced compatibility. Thrust provides a rich collection of data parallel primitives such as scan, sort, and reduce, which can be composed together to implement complex algorithms with concise, readable source code. Cornell Virtual Workshop: Memory Architecture Therefore, in terms of wxw tiles, A is a column matrix, B is a row matrix, and C is their outer product; see Figure 11. While NVIDIA GPUs are frequently associated with graphics, they are also powerful arithmetic engines capable of running thousands of lightweight threads in parallel. Testing of all parameters of each product is not necessarily performed by NVIDIA. Data transfers between the host and the device using cudaMemcpy() are blocking transfers; that is, control is returned to the host thread only after the data transfer is complete. Each new version of NVML is backward-compatible. To achieve high memory bandwidth for concurrent accesses, shared memory is divided into equally sized memory modules (banks) that can be accessed simultaneously. Mapping Persistent data accesses to set-aside L2 in sliding window experiment. Functions following the __functionName() naming convention map directly to the hardware level. These instructions also avoid using extra registers for memory copies and can also bypass the L1 cache. 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. This is evident from the saw tooth curves. nvidia-smi ships with NVIDIA GPU display drivers on Linux, and with 64-bit Windows Server 2008 R2 and Windows 7. nvidia-smi can output queried information as XML or as human-readable plain text either to standard output or to a file. For exponentiation using base 2 or 10, use the functions exp2() or expf2() and exp10() or expf10() rather than the functions pow() or powf(). If you want to communicate (i.e. The interface is augmented to retrieve either the PTX or cubin if an actual architecture is specified. In this case, no warp diverges because the controlling condition is perfectly aligned with the warps. Kernels can be written using the CUDA instruction set architecture, called PTX, which is described in the PTX reference manual. Information published by NVIDIA regarding third-party products or services does not constitute a license from NVIDIA to use such products or services or a warranty or endorsement thereof. On parallel systems, it is possible to run into difficulties not typically found in traditional serial-oriented programming. Loop Counters Signed vs. Unsigned, 11.1.5. Unified memory: supports seamless access to buffers or objects from multiple GPUs and CPUs. Customer should obtain the latest relevant information before placing orders and should verify that such information is current and complete. Therefore, to accurately measure the elapsed time for a particular call or sequence of CUDA calls, it is necessary to synchronize the CPU thread with the GPU by calling cudaDeviceSynchronize() immediately before starting and stopping the CPU timer. The NVIDIA nvcc compiler driver converts .cu files into C++ for the host system and CUDA assembly or binary instructions for the device. Its result will often differ slightly from results obtained by doing the two operations separately. Modern NVIDIA GPUs can support up to 2048 active threads concurrently per multiprocessor (see Features and Specifications of the CUDA C++ Programming Guide) On GPUs with 80 multiprocessors, this leads to more than 160,000 concurrently active threads. Binary compatibility for cubins is guaranteed from one compute capability minor revision to the next one, but not from one compute capability minor revision to the previous one or across major compute capability revisions. Like the other calls in this listing, their specific operation, parameters, and return values are described in the CUDA Toolkit Reference Manual. The actual memory throughput shows how close the code is to the hardware limit, and a comparison of the effective or requested bandwidth to the actual bandwidth presents a good estimate of how much bandwidth is wasted by suboptimal coalescing of memory accesses (see Coalesced Access to Global Memory). 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. If this happens, the different execution paths must be executed separately; this increases the total number of instructions executed for this warp. 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.) Using a profiler, the developer can identify such hotspots and start to compile a list of candidates for parallelization. TO THE EXTENT NOT PROHIBITED BY LAW, IN NO EVENT WILL NVIDIA BE LIABLE FOR ANY DAMAGES, INCLUDING WITHOUT LIMITATION ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, PUNITIVE, OR CONSEQUENTIAL DAMAGES, HOWEVER CAUSED AND REGARDLESS OF THE THEORY OF LIABILITY, ARISING OUT OF ANY USE OF THIS DOCUMENT, EVEN IF NVIDIA HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. Increased L2 capacity and L2 Residency Controls, 1.4.2.3. The simple remedy is to pad the shared memory array so that it has an extra column, as in the following line of code. 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. The available profiling tools are invaluable for guiding this process, as they can help suggest a next-best course of action for the developers optimization efforts and provide references into the relevant portions of the optimization section of this guide. From supercomputers to mobile phones, modern processors increasingly rely on parallelism to provide performance. However, the device is based on a distinctly different design from the host system, and its important to understand those differences and how they determine the performance of CUDA applications in order to use CUDA effectively. C++-style convenience wrappers (cuda_runtime.h) built on top of the C-style functions. This capability (combined with thread synchronization) has a number of uses, such as user-managed data caches, high-performance cooperative parallel algorithms (parallel reductions, for example), and tofacilitate global memory coalescing in cases where it would otherwise not be possible. libcuda.so on Linux systems). The details of various CPU timing approaches are outside the scope of this document, but developers should always be aware of the resolution their timing calls provide. Using UVA, on the other hand, the physical memory space to which a pointer points can be determined simply by inspecting the value of the pointer using cudaPointerGetAttributes(). This is done by carefully choosing the execution configuration of each kernel launch. It is however usually more effective to use a high-level programming language such as C++. For a warp of threads, col represents sequential columns of the transpose of A, and therefore col*TILE_DIM represents a strided access of global memory with a stride of w, resulting in plenty of wasted bandwidth. Between 128 and 256 threads per block is a good initial range for experimentation with different block sizes. On the other hand, if the data is only accessed once, such data accesses can be considered to be streaming. Asynchronous and Overlapping Transfers with Computation, 9.2.1.2. If cudaGetDeviceCount() reports an error, the application should fall back to an alternative code path. For small integer powers (e.g., x2 or x3), explicit multiplication is almost certainly faster than the use of general exponentiation routines such as pow(). 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. Finally, this product is divided by 109 to convert the result to GB/s. This spreadsheet, shown in Figure 15, is called CUDA_Occupancy_Calculator.xls and is located in the tools subdirectory of the CUDA Toolkit installation. The goal is to maximize the use of the hardware by maximizing bandwidth. The maximum number of registers per thread can be set manually at compilation time per-file using the -maxrregcount option or per-kernel using the __launch_bounds__ qualifier (see Register Pressure). For GPUs with compute capability 8.6, shared memory capacity per SM is 100 KB. 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. So while the impact is still evident it is not as large as we might have expected. The NVIDIA Ampere GPU architectures Streaming Multiprocessor (SM) provides the following improvements over Volta and Turing. Reinitialize the GPU hardware and software state via a secondary bus reset. Hardware utilization can also be improved in some cases by designing your application so that multiple, independent kernels can execute at the same time. 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 throughput of individual arithmetic operations is detailed in the CUDA C++ Programming Guide. 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. The larger N is(that is, the greater the number of processors), the smaller the P/N fraction. Users should refer to the CUDA headers and documentation for new CUDA APIs introduced in a release. For example, servers that have two 32 core processors can run only 64 threads concurrently (or small multiple of that if the CPUs support simultaneous multithreading). 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. 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. Shared memory accesses, in counterpoint, are usually worth optimizing only when there exists a high degree of bank conflicts. But before executing this final line in which each thread accesses data in shared memory that was written by another thread, remember that we need to make sure all threads have completed the loads to shared memory, by calling__syncthreads(). 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. In such cases, kernels with 32x32 or 64x16 threads can be launched with each thread processing four elements of the shared memory array. 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 the kernel launch, specify the total shared memory needed, as in the following. Moreover, in such cases, the argument-reduction code uses local memory, which can affect performance even more because of the high latency of local memory. For recent versions of CUDA hardware, misaligned data accesses are not a big issue. Mutually exclusive execution using std::atomic? Because the driver may interleave execution of CUDA calls from other non-default streams, calls in other streams may be included in the timing. For applications that need additional functionality or performance beyond what existing parallel libraries or parallelizing compilers can provide, parallel programming languages such as CUDA C++ that integrate seamlessly with existing sequential code are essential. For more information on this pragma, refer to the CUDA C++ Programming Guide. The performance of the sliding-window benchmark with fixed hit-ratio of 1.0. Alternatively, the nvcc command-line option -arch=sm_XX can be used as a shorthand equivalent to the following more explicit -gencode= command-line options described above: However, while the -arch=sm_XX command-line option does result in inclusion of a PTX back-end target by default (due to the code=compute_XX target it implies), it can only specify a single target cubin architecture at a time, and it is not possible to use multiple -arch= options on the same nvcc command line, which is why the examples above use -gencode= explicitly. 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. The CUDA Toolkit includes a number of such libraries that have been fine-tuned for NVIDIA CUDA GPUs, such as cuBLAS, cuFFT, and so on. Access to shared memory is much faster than global memory access because it is located on a chip. The CUDA Driver API has a versioned C-style ABI, which guarantees that applications that were running against an older driver (for example CUDA 3.2) will still run and function correctly against a modern driver (for example one shipped with CUDA 11.0).