The amount of performance benefit an application will realize by running on CUDA depends entirely on the extent to which it can be parallelized. Recommendations for taking advantage of minor version compatibility in your application, 16.4. When using CPU timers, it is critical to remember that many CUDA API functions are asynchronous; that is, they return control back to the calling CPU thread prior to completing their work. Memory Access 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. Low Priority: Make it easy for the compiler to use branch predication in lieu of loops or control statements. The simple remedy is to pad the shared memory array so that it has an extra column, as in the following line of code. The async-copy does not require the copy_count parameter to be a multiple of 4, to maximize performance through compiler optimizations. An additional set of Perl and Python bindings are provided for the NVML API. These memory spaces include global, local, shared, texture, and registers, as shown in Figure 2. 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. By exposing parallelism to the compiler, directives allow the compiler to do the detailed work of mapping the computation onto the parallel architecture. Register storage enables threads to keep local variables nearby for low-latency access. On the other hand, if the data is only accessed once, such data accesses can be considered to be streaming. 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. 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. Access to shared memory is much faster than global memory access because it is located on chip. Choosing the execution configuration parameters should be done in tandem; however, there are certain heuristics that apply to each parameter individually. 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. 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). Current GPUs can simultaneously process asynchronous data transfers and execute kernels. Other company and product names may be trademarks of the respective companies with which they are associated. (BFloat16 only supports FP32 as accumulator), unsigned char/signed char (8-bit precision). The major and minor revision numbers of the compute capability are shown on the seventh line of Figure 16. 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. The high-priority recommendations from those guides are as follows: Find ways to parallelize sequential code. On the other hand, some applications designs will require some amount of refactoring to expose their inherent parallelism. 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 capability makes them well suited to computations that can leverage parallel execution. Please refer to the EULA for details. Throughout this guide, specific recommendations are made regarding the design and implementation of CUDA C++ code. This is particularly beneficial to kernels that frequently call __syncthreads(). The NVIDIA System Management Interface (nvidia-smi) is a command line utility that aids in the management and monitoring of NVIDIA GPU devices. Once we have located a hotspot in our applications profile assessment and determined that custom code is the best approach, we can use CUDA C++ to expose the parallelism in that portion of our code as a CUDA kernel. Support for TF32 Tensor Core, through HMMA instructions. If static linking against the CUDA Runtime is impractical for some reason, then a dynamically-linked version of the CUDA Runtime library is also available. If from any of the four 32-byte segments only a subset of the words are requested (e.g. Both correctable single-bit and detectable double-bit errors are reported. CUDA Memory Rules Currently can only transfer data from host to global (and constant memory) and not host directly to shared. Therefore, any memory load or store of n addresses that spans n distinct memory banks can be serviced simultaneously, yielding an effective bandwidth that is n times as high as the bandwidth of a single bank. 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. These results should be compared with those in Table 2. This code reverses the data in a 64-element array using shared memory. 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. 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. This means that in one of these devices, for a multiprocessor to have 100% occupancy, each thread can use at most 32 registers. We cannot declare these directly, but small static allocations go . Providing the two argument version of __launch_bounds__(maxThreadsPerBlock,minBlocksPerMultiprocessor) can improve performance in some cases. Verify that your library doesnt leak dependencies, breakages, namespaces, etc. Higher occupancy does not always equate to higher performance-there is a point above which additional occupancy does not improve performance. (It should be mentioned that it is not possible to overlap a blocking transfer with an asynchronous transfer, because the blocking transfer occurs in the default stream, so it will not begin until all previous CUDA calls complete. Strong scaling is a measure of how, for a fixed overall problem size, the time to solution decreases as more processors are added to a system. To check for errors occurring during kernel launches using the <<<>>> syntax, which does not return any error code, the return code of cudaGetLastError() should be checked immediately after the kernel launch. 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. 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. Many software libraries and applications built on top of CUDA (e.g. CUDA kernel and thread hierarchy All rights reserved. Unified memory: supports seamless access to buffers or objects from multiple GPUs and CPUs. To analyze performance, it is necessary to consider how warps access global memory in the for loop. CUDA applications are built against the CUDA Runtime library, which handles device, memory, and kernel management. All threads within one block see the same shared memory array . To assist with this, the CUDA Driver API provides methods to access and manage a special context on each GPU called the primary context. 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. Shared memory Bank Conflicts: Shared memory bank conflicts exist and are common for the strategy used. Because kernel1 and kernel2 are executed in different, non-default streams, a capable device can execute the kernels at the same time. In fact, local memory is off-chip. A portion of the L2 cache can be set aside for persistent accesses to a data region in global memory. The combined L1 cache capacity for GPUs with compute capability 8.6 is 128 KB. 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. Furthermore, there should be multiple active blocks per multiprocessor so that blocks that arent waiting for a __syncthreads() can keep the hardware busy. However, bank conflicts occur when copying the tile from global memory into shared memory. By comparison, threads on GPUs are extremely lightweight. By default the 48KBshared memory setting is used. When a CUDA kernel accesses a data region in the global memory repeatedly, such data accesses can be considered to be persisting. The SONAME of the library against which the application was built must match the filename of the library that is redistributed with the application. Applications with remote random accesses may want to constrain the remotely accessed region to 64 GB for each peer GPU. NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any time without notice. I think this pretty much implies that you are going to have the place the heads of each queue in global memory. As illustrated in Figure 7, non-unit-stride global memory accesses should be avoided whenever possible. The discussions in this guide all use the C++ programming language, so you should be comfortable reading C++ code. Medium Priority: The number of threads per block should be a multiple of 32 threads, because this provides optimal computing efficiency and facilitates coalescing. In contrast with cudaMemcpy(), the asynchronous transfer version requires pinned host memory (see Pinned Memory), and it contains an additional argument, a stream ID. Do new devs get fired if they can't solve a certain bug? 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. Asynchronous and Overlapping Transfers with Computation, 9.2.1.2. 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. Applying Strong and Weak Scaling, 6.3.2. To understand the effect of hitRatio and num_bytes, we use a sliding window micro benchmark. For example, if the hitRatio value is 0.6, 60% of the memory accesses in the global memory region [ptr..ptr+num_bytes) have the persisting property and 40% of the memory accesses have the streaming property. There are many possible approaches to profiling the code, but in all cases the objective is the same: to identify the function or functions in which the application is spending most of its execution time. Access to shared memory is much faster than global memory access because it is located on a chip. On PCIe x16 Gen3 cards, for example, pinned memory can attain roughly 12 GB/s transfer rates. Obtaining the right answer is clearly the principal goal of all computation. This is done by carefully choosing the execution configuration of each kernel launch. CUDA shared memory writes incur unexplainable long latency, CUDA atomic function usage with volatile shared memory. 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. For devices with compute capability of 2.0 or greater, the Visual Profiler can be used to collect several different memory throughput measures. CUDA shared memory not faster than global? Weak Scaling and Gustafsons Law, 3.1.3.3. Instead, all instructions are scheduled, but a per-thread condition code or predicate controls which threads execute the instructions. Medium Priority: To hide latency arising from register dependencies, maintain sufficient numbers of active threads per multiprocessor (i.e., sufficient occupancy). 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 action leads to a load of eight L2 cache segments per warp on the Tesla V100 (compute capability 7.0). Therefore, the compiler can optimize more aggressively with signed arithmetic than it can with unsigned arithmetic. Overall, developers can expect similar occupancy as on Volta without changes to their application. Flow control instructions (if, switch, do, for, while) can significantly affect the instruction throughput by causing threads of the same warp to diverge; that is, to follow different execution paths. 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. 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. It supports a number of command-line parameters, of which the following are especially useful for optimization and related best practices: -maxrregcount=N specifies the maximum number of registers kernels can use at a per-file level. Recovering from a blunder I made while emailing a professor. Reading from a texture while writing to its underlying global memory array in the same kernel launch should be avoided because the texture caches are read-only and are not invalidated when the associated global memory is modified. Strong Scaling and Amdahls Law, 3.1.3.2. One of several factors that determine occupancy is register availability. 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). A CUDA context is a software environment that manages memory and other resources 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. In many cases, the amount of shared memory required by a kernel is related to the block size that was chosen, but the mapping of threads to shared memory elements does not need to be one-to-one. This is called just-in-time compilation (JIT). Asynchronous Data Copy from Global Memory to Shared Memory, 1.4.1.3. When attempting to optimize CUDA code, it pays to know how to measure performance accurately and to understand the role that bandwidth plays in performance measurement. As a result, this section discusses size but not dimension. 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. 2) In one block I need to load into shared memory the queues of other blocks. To illustrate the effect of strided access on effective bandwidth, see the kernel strideCopy() in A kernel to illustrate non-unit stride data copy, which copies data with a stride of stride elements between threads from idata to odata. At a minimum, you would need some sort of selection process that can access the heads of each queue. This cost has several ramifications: The complexity of operations should justify the cost of moving data to and from the device. The NVIDIA Ampere GPU architectures Streaming Multiprocessor (SM) provides the following improvements over Volta and Turing. Randomly accessing. For a listing of some of these tools, see https://developer.nvidia.com/cluster-management. An exception is the case where all threads in a warp address the same shared memory address, resulting in a broadcast. Connect and share knowledge within a single location that is structured and easy to search. These recommendations are categorized by priority, which is a blend of the effect of the recommendation and its scope. 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.

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