cuda shared memory between blocks

The two kernels are very similar, differing only in how the shared memory arrays are declared and how the kernels are invoked. 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. On the other hand, if the data is only accessed once, such data accesses can be considered to be streaming. Note that the process used for validating numerical results can easily be extended to validate performance results as well. Static linking makes the executable slightly larger, but it ensures that the correct version of runtime library functions are included in the application binary without requiring separate redistribution of the CUDA Runtime library. Consider the following kernel code and access window parameters, as the implementation of the sliding window experiment. It also disables single-precision denormal support and lowers the precision of single-precision division in general. For more details on the new warp wide reduction operations refer to Warp Reduce Functions in the CUDA C++ Programming Guide. Then, thread A wants to read Bs element from shared memory, and vice versa. See the Application Note on CUDA for Tegra for details. Follow semantic versioning for your librarys soname. The Perl bindings are provided via CPAN and the Python bindings via PyPI. 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. All CUDA Runtime API calls return an error code of type cudaError_t; the return value will be equal to cudaSuccess if no errors have occurred. 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. While multiple contexts (and their associated resources such as global memory allocations) can be allocated concurrently on a given GPU, only one of these contexts can execute work at any given moment on that GPU; contexts sharing the same GPU are time-sliced. 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). Threads on a CPU are generally heavyweight entities. The other three kernels in this example use dynamically allocated shared memory, which can be used when the amount of shared memory is not known at compile time. The cudaChooseDevice() function can be used to select the device that most closely matches a desired set of features. If there are differences, then those differences will be seen early and can be understood in the context of a simple function. rev2023.3.3.43278. Strong Scaling and Amdahls Law describes strong scaling, which allows us to set an upper bound for the speedup with a fixed problem size. As the stride increases, the effective bandwidth decreases until the point where 32 32-byte segments are loaded for the 32 threads in a warp, as indicated in Figure 7. If multiple CUDA application processes access the same GPU concurrently, this almost always implies multiple contexts, since a context is tied to a particular host process unless Multi-Process Service is in use. Computing a row of a tile in C using one row of A and an entire tile of B.. I think this pretty much implies that you are going to have the place the heads of each queue in global memory. 32/48/64/96/128K depending on the GPU and current configuration) and each block can use a chunk of it by declaring shared memory. (tens of kBs capacity) Global memory is main memory (GDDR,HBM, (1-32 GB)) and data is cached by L2,L1 caches. This is called just-in-time compilation (JIT). Page-locked or pinned memory transfers attain the highest bandwidth between the host and the device. Therefore, the total number of links available is increased to twelve in A100, versus six in V100, yielding 600 GB/s bidirectional bandwidth versus 300 GB/s for V100. This is evident from the saw tooth curves. Understanding Scaling discusses the potential benefit we might expect from such parallelization. They produce equivalent results. High Priority: To maximize developer productivity, profile the application to determine hotspots and bottlenecks. The context is explicit in the CUDA Driver API but is entirely implicit in the CUDA Runtime API, which creates and manages contexts automatically. The context encapsulates kernel launches and memory allocations for that GPU as well as supporting constructs such as the page tables. For example, improving occupancy from 66 percent to 100 percent generally does not translate to a similar increase in performance. Then with a tile size of 32, the shared memory buffer will be of shape [32, 32]. 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. You want to sort all the queues before you collect them. For recent versions of CUDA hardware, misaligned data accesses are not a big issue. For slightly better performance, however, they should instead be declared as signed. 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. The kernel is executed within a loop in host code that varies the parameter offset from 0 to 32. 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(). Multiple kernels executing at the same time is known as concurrent kernel execution. Accesses to the remaining data of the memory region (i.e., streaming data) are considered normal or streaming accesses and will thus use the remaining 10 MB of the non set-aside L2 portion (unless part of the L2 set-aside portion is unused). However, a few rules of thumb should be followed: Threads per block should be a multiple of warp size to avoid wasting computation on under-populated warps and to facilitate coalescing. Weak Scaling and Gustafsons Law, 3.1.3.3. Is it possible to share a Cuda context between applications - Introduction CUDA is a parallel computing platform and programming model created by Nvidia. Note also that whenever sine and cosine of the same argument are computed, the sincos family of instructions should be used to optimize performance: __sincosf() for single-precision fast math (see next paragraph). A key concept in this effort is occupancy, which is explained in the following sections. This approach permits some overlapping of the data transfer and execution. An additional set of Perl and Python bindings are provided for the NVML API. Missing dependencies is also a binary compatibility break, hence you should provide fallbacks or guards for functionality that depends on those interfaces. Checking these things frequently as an integral part of our cyclical APOD process will help ensure that we achieve the desired results as rapidly as possible. and one element in the streaming data section. However, it also can act as a constraint on occupancy. 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. For Windows, the /DELAY option is used; this requires that the application call SetDllDirectory() before the first call to any CUDA API function in order to specify the directory containing the CUDA DLLs. Is it possible to share a Cuda context between applications But this technique is still useful for other access patterns, as Ill show in the next post.). This is the default if using nvcc to link in CUDA 5.5 and later. 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). Handling New CUDA Features and Driver APIs, 15.4.1.4. Lets say that two threads A and B each load a data element from global memory and store it to shared memory. The following example illustrates the basic technique. Although each of these instructions is scheduled for execution, only the instructions with a true predicate are actually executed. A more robust approach is to selectively introduce calls to fast intrinsic functions only if merited by performance gains and where altered behavior can be tolerated. When our CUDA 11.1 application (i.e. The difference is in how threads in a half warp access elements of A in the second term, a[col*TILE_DIM+i], for each iteration i. At a minimum, you would need some sort of selection process that can access the heads of each queue. This ensures your code is compatible. No license, either expressed or implied, is granted under any NVIDIA patent right, copyright, or other NVIDIA intellectual property right under this document. My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? The one exception here is when multiple threads in a warp address the same shared memory location, resulting in a broadcast. A further improvement can be made to how Using shared memory to improve the global memory load efficiency in matrix multiplication deals with matrix B. Shared memory is specified by the device architecture and is measured on per-block basis. For example, the asyncEngineCount field of the device property structure indicates whether overlapping kernel execution and data transfers is possible (and, if so, how many concurrent transfers are possible); likewise, the canMapHostMemory field indicates whether zero-copy data transfers can be performed. 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. (The performance advantage sinpi() has over sin() is due to simplified argument reduction; the accuracy advantage is because sinpi() multiplies by only implicitly, effectively using an infinitely precise mathematical rather than a single- or double-precision approximation thereof.). sm_80) rather than a virtual architecture (e.g. 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. Under UVA, pinned host memory allocated with cudaHostAlloc() will have identical host and device pointers, so it is not necessary to call cudaHostGetDevicePointer() for such allocations. Therefore, choosing sensible thread block sizes, such as multiples of the warp size (i.e., 32 on current GPUs), facilitates memory accesses by warps that are properly aligned. Instead, strategies can be applied incrementally as they are learned. Memory Access 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. To achieve high memory bandwidth for concurrent accesses, shared memory is divided into equally sized memory modules (banks) that can be accessed simultaneously. 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. Improvement by reading additional data into shared memory. Replacing broken pins/legs on a DIP IC package. Depending on the value of the num_bytes parameter and the size of L2 cache, one may need to tune the value of hitRatio to avoid thrashing of L2 cache lines. The new Tensor Cores use a larger base matrix size and add powerful new math modes including: Support for FP64 Tensor Core, using new DMMA instructions. For certain devices of compute capability 5.2, L1-caching of accesses to global memory can be optionally enabled. Awareness of how instructions are executed often permits low-level optimizations that can be useful, especially in code that is run frequently (the so-called hot spot in a program). The performance of the above kernel is shown in the chart below. 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. CUDA: Explainer of a kernel with 2D blocks, shared memory, atomics -use_fast_math compiler option of nvcc coerces every functionName() call to the equivalent __functionName() call. 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. On the other hand, some applications designs will require some amount of refactoring to expose their inherent parallelism. Concurrent kernel execution is described below. 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. For purposes of calculating occupancy, the number of registers used by each thread is one of the key factors. In such cases, kernels with 32x32 or 64x16 threads can be launched with each thread processing four elements of the shared memory array. For global memory accesses, this actual throughput is reported by the Global Load Throughput and Global Store Throughput values. If the PTX is also not available, then the kernel launch will fail. Not the answer you're looking for? Understanding which type of scaling is most applicable to an application is an important part of estimating speedup. CUDA Binary (cubin) Compatibility, 15.4. The CUDA runtime has relaxed the minimum driver version check and thus no longer requires a driver upgrade when moving to a new minor release. In particular, a larger block size does not imply a higher occupancy. Occupancy is the ratio of the number of active warps per multiprocessor to the maximum number of possible active warps. Best performance with synchronous copy is achieved when the copy_count parameter is a multiple of 4 for all three element sizes. With the CUDA Driver API, a CUDA application process can potentially create more than one context for a given GPU. Such a pattern is shown in Figure 3. Using Kolmogorov complexity to measure difficulty of problems? Using a profiler, the developer can identify such hotspots and start to compile a list of candidates for parallelization. The __pipeline_wait_prior(0) will wait until all the instructions in the pipe object have been executed. - the incident has nothing to do with me; can I use this this way? Functions following functionName() naming convention are slower but have higher accuracy (e.g., sinf(x) and expf(x)). Performance Improvements Optimizing C = AB Matrix Multiply In our use case, BLOCK_SIZE + 2 * RADIUS = $1024 + 2 \times 6000$ = $13024$ and the size of an int is $4$ Byte, therefore, the shared memory required is $17024 \times 4 / 1024$ = $50.875$ KB, which is larger than the maximum static shared memory we could have. Data Transfer Between Host and Device, 9.1.2. Having identified the hotspots and having done the basic exercises to set goals and expectations, the developer needs to parallelize the code. Now that we are working block by block, we should use shared memory. Recovering from a blunder I made while emailing a professor. The latter become even more expensive (about an order of magnitude slower) if the magnitude of the argument x needs to be reduced. The reciprocal square root should always be invoked explicitly as rsqrtf() for single precision and rsqrt() for double precision. In CUDA there is no defined global synchronization mechanism except the kernel launch. This limitation is not specific to CUDA, but an inherent part of parallel computation on floating-point values. Certain functionality might not be available so you should query where applicable. This document is not a commitment to develop, release, or deliver any Material (defined below), code, or functionality. (Consider what would happen to the memory addresses accessed by the second, third, and subsequent thread blocks if the thread block size was not a multiple of warp size, for example.). I think this pretty much implies that you are going to have the place the heads of each queue in global memory. 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. Another benefit of its union with shared memory, similar to Volta L1 is improvement in terms of both latency and bandwidth. shared memory needed per block is lenP + lenS (where lenS is your blocksize + patternlength) the kernel assumes that gridDim.x * blockDim.x = lenT (the same as version 1) we can copy into shared memory in parallel (no need for for loops if you have enough threads) Share Improve this answer Follow edited Apr 15, 2011 at 19:59 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 third generation of NVIDIAs high-speed NVLink interconnect is implemented in A100 GPUs, which significantly enhances multi-GPU scalability, performance, and reliability with more links per GPU, much faster communication bandwidth, and improved error-detection and recovery features. At a minimum, you would need some sort of selection process that can access the heads of each queue. With each generation of NVIDIA processors, new features are added to the GPU that CUDA can leverage. This situation is not different from what is available today where developers use macros to compile out features based on CUDA versions. Data Transfer Between Host and Device provides further details, including the measurements of bandwidth between the host and the device versus within the device proper. An example would be modeling how two molecules interact with each other, where the molecule sizes are fixed. Connect and share knowledge within a single location that is structured and easy to search. Comparing Performance of Synchronous vs Asynchronous Copy from Global Memory to Shared Memory. As for optimizing instruction usage, the use of arithmetic instructions that have low throughput should be avoided. 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. .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. No contractual obligations are formed either directly or indirectly by this document. 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. This document is not a commitment to develop, release, or deliver any Material (defined below), code, or functionality. Strong Scaling and Amdahls Law, 3.1.3.2. Please note that new versions of nvidia-smi are not guaranteed to be backward-compatible with previous versions. Instructions with a false predicate do not write results, and they also do not evaluate addresses or read operands. 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. Now Let's Look at Shared Memory Common Programming Pattern (5.1.2 of CUDA manual) - Load data into shared memory - Synchronize (if necessary) - Operate on data in shared memory - Synchronize (if necessary) - Write intermediate results to global memory - Repeat until done Shared memory Global memory Familiar concept?? Declare shared memory in CUDA C/C++ device code using the__shared__variable declaration specifier. 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. Therefore, to get the largest speedup for a fixed problem size, it is worthwhile to spend effort on increasing P, maximizing the amount of code that can be parallelized. All rights reserved. A slightly related but important topic is one of application binary compatibility across GPU architectures in CUDA. Dont expose ABI structures that can change. Per thread resources required by a CUDA kernel might limit the maximum block size in an unwanted way. Having completed the GPU acceleration of one or more components of the application it is possible to compare the outcome with the original expectation. Overall, developers can expect similar occupancy as on Volta without changes to their application. While a binary compiled for 8.0 will run as is on 8.6, it is recommended to compile explicitly for 8.6 to benefit from the increased FP32 throughput. The operating system must swap threads on and off CPU execution channels to provide multithreading capability. 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). Last updated on Feb 27, 2023. cudaFuncAttributePreferredSharedMemoryCarveout, 1. Since shared memory is shared amongst threads in a thread block, it provides a mechanism for threads to cooperate. The SONAME of the library against which the application was built must match the filename of the library that is redistributed with the application. This kernel has an effective bandwidth of 144.4 GB/s on an NVIDIA Tesla V100. To allocate an array in shared memory we . In the NVIDIA Ampere GPU architecture remote NVLINK accesses go through a Link TLB on the remote GPU. PTX programs are translated at load time to the target hardware instruction set via the JIT Compiler which is part of the CUDA driver. Access to shared memory is much faster than global memory access because it is located on chip. See Math Libraries. CUDA C++ provides a simple path for users familiar with the C++ programming language to easily write programs for execution by the device. In the code in Zero-copy host code, kernel() can reference the mapped pinned host memory using the pointer a_map in exactly the same was as it would if a_map referred to a location in device memory. 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. In this scenario, CUDA initialization returns an error due to the minimum driver requirement. This advantage is increased when several powers of the same base are needed (e.g., where both x2 and x5 are calculated in close proximity), as this aids the compiler in its common sub-expression elimination (CSE) optimization. For example, a 64-bit application linked to cuBLAS 5.5 will look for cublas64_55.dll at runtime, so this is the file that should be redistributed with that application, even though cublas.lib is the file that the application is linked against.