cuda shared memory between blocks cuda shared memory between blocks

The results of these calculations can frequently differ from pure 64-bit operations performed on the CUDA device. Any CPU timer can be used to measure the elapsed time of a CUDA call or kernel execution. Certain functionality might not be available so you should query where applicable. Using Shared Memory in CUDA Fortran | NVIDIA Technical Blog 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. 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. All CUDA compute devices follow the IEEE 754 standard for binary floating-point representation, with some small exceptions. The cudaDeviceCanAccessPeer() can be used to determine if peer access is possible between any pair of GPUs. Improvement by reading additional data into shared memory. 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. Data copied from global memory to shared memory using asynchronous copy instructions can be cached in the L1 cache or the L1 cache can be optionally bypassed. Low Priority: Avoid automatic conversion of doubles to floats. This approach should be used even if one of the steps in a sequence of calculations could be performed faster on the host. math libraries or deep learning frameworks) do not have a direct dependency on the CUDA runtime, compiler or driver. Both the CUDA driver and the CUDA runtime are not source compatible across the different SDK releases. 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. Like the other calls in this listing, their specific operation, parameters, and return values are described in the CUDA Toolkit Reference Manual. See Version Management for details on how to query the available CUDA software API versions. New APIs can be added in minor versions. For most purposes, the key point is that the larger the parallelizable portion P is, the greater the potential speedup. Cornell Virtual Workshop: Memory Architecture For devices of compute capability 2.x, there are two settings, 48KBshared memory / 16KB L1 cache, and 16KB shared memory / 48KB L1 cache. 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. Because shared memory is shared by threads in a thread block, it provides a mechanism for threads to cooperate. 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. For more details refer to the memcpy_async section in the CUDA C++ Programming Guide. The effective bandwidth of this kernel is 140.2 GB/s on an NVIDIA Tesla V100.These results are lower than those obtained by the final kernel for C = AB. Otherwise, five 32-byte segments are loaded per warp, and we would expect approximately 4/5th of the memory throughput achieved with no offsets. For branches including just a few instructions, warp divergence generally results in marginal performance losses. An upgraded driver matching the CUDA runtime version is currently required for those APIs. 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. 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. NVIDIA products are not designed, authorized, or warranted to be suitable for use in medical, military, aircraft, space, or life support equipment, nor in applications where failure or malfunction of the NVIDIA product can reasonably be expected to result in personal injury, death, or property or environmental damage. To use other CUDA APIs introduced in a minor release (that require a new driver), one would have to implement fallbacks or fail gracefully. In this guide, they represent a typical case. 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. 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. For slightly better performance, however, they should instead be declared as signed. 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. It is possible to rearrange the collection of installed CUDA devices that will be visible to and enumerated by a CUDA application prior to the start of that application by way of the CUDA_VISIBLE_DEVICES environment variable. This is advantageous with regard to both accuracy and performance. This utility allows administrators to query GPU device state and, with the appropriate privileges, permits administrators to modify GPU device state. This approach will tend to provide the best results for the time invested and will avoid the trap of premature optimization. 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?? Like Volta, the NVIDIA Ampere GPU architecture combines the functionality of the L1 and texture caches into a unified L1/Texture cache which acts as a coalescing buffer for memory accesses, gathering up the data requested by the threads of a warp prior to delivery of that data to the warp. A Sequential but Misaligned Access Pattern, 9.2.2.2. An optimized handling of strided accesses using coalesced reads from global memory. In reality, most applications do not exhibit perfectly linear strong scaling, even if they do exhibit some degree of strong scaling. Assess, Parallelize, Optimize, Deploy, 3.1.3.1. When accessing uncached local or global memory, there are hundreds of clock cycles of memory latency. Not requiring driver updates for new CUDA releases can mean that new versions of the software can be made available faster to users. The last argument to the cudaMemcpyAsync() function is the stream ID, which in this case uses the default stream, stream 0. The following documents are especially important resources: In particular, the optimization section of this guide assumes that you have already successfully downloaded and installed the CUDA Toolkit (if not, please refer to the relevant CUDA Installation Guide for your platform) and that you have a basic familiarity with the CUDA C++ programming language and environment (if not, please refer to the CUDA C++ Programming Guide). Managing your GPU cluster will help achieve maximum GPU utilization and help you and your users extract the best possible performance. 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. Medium Priority: To hide latency arising from register dependencies, maintain sufficient numbers of active threads per multiprocessor (i.e., sufficient occupancy). CUDA Shared Memory Capacity - Lei Mao's Log Book For devices of compute capability 6.0 or higher, the requirements can be summarized quite easily: the concurrent accesses of the threads of a warp will coalesce into a number of transactions equal to the number of 32-byte transactions necessary to service all of the threads of the warp. In Overlapping computation and data transfers, the memory copy and kernel execution occur sequentially. An explicit __syncwarp() can be used to guarantee that the warp has reconverged for subsequent instructions. Before tackling other hotspots to improve the total speedup, the developer should consider taking the partially parallelized implementation and carry it through to production. Other company and product names may be trademarks of the respective companies with which they are associated. This typically involves arithmetic on large data sets (such as matrices) where the same operation can be performed across thousands, if not millions, of elements at the same time. 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. Consequently, its important to understand the characteristics of the architecture. Pinned memory is allocated using the cudaHostAlloc() functions in the Runtime API. Note that no bank conflict occurs if only one memory location per bank is accessed by a half warp of threads. Therefore, the compiler can optimize more aggressively with signed arithmetic than it can with unsigned arithmetic. On the other hand, some applications designs will require some amount of refactoring to expose their inherent parallelism. Is it suspicious or odd to stand by the gate of a GA airport watching the planes? In contrast with cudaMemcpy(), the asynchronous transfer version requires pinned host memory (see Pinned Memory), and it contains an additional argument, a stream ID. Prefer shared memory access where possible. Global memory loads and stores by threads of a warp are coalesced by the device into as few as possible transactions. This metric is occupancy. Access to shared memory is much faster than global memory access because it is located on a chip. PTX programs are translated at load time to the target hardware instruction set via the JIT Compiler which is part of the CUDA driver. Aside from memory bank conflicts, there is no penalty for non-sequential or unaligned accesses by a warp in shared memory. NVIDIA Ampere GPU Architecture Tuning Guide A threads execution can only proceed past a __syncthreads() after all threads in its block have executed the __syncthreads(). Hence, its important to design your application to use threads and blocks in a way that maximizes hardware utilization and to limit practices that impede the free distribution of work. Prior to UVA, an application had to keep track of which pointers referred to device memory (and for which device) and which referred to host memory as a separate bit of metadata (or as hard-coded information in the program) for each pointer. In certain addressing situations, reading device memory through texture fetching can be an advantageous alternative to reading device memory from global or constant memory. 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. Because the minimum memory transaction size is larger than most word sizes, the actual memory throughput required for a kernel can include the transfer of data not used by the kernel. When using NVRTC, it is recommended that the resulting PTX code is first transformed to the final device code via the steps outlined by the PTX user workflow. When multiple threads in a block use the same data from global memory, shared memory can be used to access the data from global memory only once. This is a requirement for good performance on CUDA: the software must use a large number (generally thousands or tens of thousands) of concurrent threads. Computing a row of a tile. On Linux systems, the CUDA driver and kernel mode components are delivered together in the NVIDIA display driver package. Using shared memory to coalesce global reads. The latter become even more expensive (about an order of magnitude slower) if the magnitude of the argument x needs to be reduced. Therefore, any memory load or store of n addresses that spans b distinct memory banks can be serviced simultaneously, yielding an effective bandwidth that is b times as high as the bandwidth of a single bank. One or more compute capability versions can be specified to the nvcc compiler while building a file; compiling for the native compute capability for the target GPU(s) of the application is important to ensure that application kernels achieve the best possible performance and are able to use the features that are available on a given generation of GPU. Using Shared Memory in CUDA C/C++ | NVIDIA Technical Blog 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 peak theoretical bandwidth between the device memory and the GPU is much higher (898 GB/s on the NVIDIA Tesla V100, for example) than the peak theoretical bandwidth between host memory and device memory (16 GB/s on the PCIe x16 Gen3). 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. Excessive use can reduce overall system performance because pinned memory is a scarce resource, but how much is too much is difficult to know in advance. It should also be noted that the CUDA math librarys complementary error function, erfcf(), is particularly fast with full single-precision accuracy. This code reverses the data in a 64-element array using shared memory. Having completed the GPU acceleration of one or more components of the application it is possible to compare the outcome with the original expectation. \times (4096/8) \times 2 \right) \div 10^{9} = 898\text{GB/s}\). Data should be kept on the device as long as possible. // Number of bytes for persisting accesses. 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. (Factorization). Kernels can be written using the CUDA instruction set architecture, called PTX, which is described in the PTX reference manual. Devices of compute capability 8.6 have 2x more FP32 operations per cycle per SM than devices of compute capability 8.0. CUDA kernel and thread hierarchy To obtain best performance in cases where the control flow depends on the thread ID, the controlling condition should be written so as to minimize the number of divergent warps. Texture references that are bound to CUDA arrays can be written to via surface-write operations by binding a surface to the same underlying CUDA array storage). I'm not sure if this will fit your overall processing. 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. It enables GPU threads to directly access host memory. As for optimizing instruction usage, the use of arithmetic instructions that have low throughput should be avoided. The NVIDIA Ampere GPU architecture allows CUDA users to control the persistence of data in L2 cache. aims to make the expression of this parallelism as simple as possible, while simultaneously enabling operation on CUDA-capable GPUs designed for maximum parallel throughput. The cudaGetDeviceCount() function can be used to query for the number of available devices. GPUs with a single copy engine can perform one asynchronous data transfer and execute kernels whereas GPUs with two copy engines can simultaneously perform one asynchronous data transfer from the host to the device, one asynchronous data transfer from the device to the host, and execute kernels. An application has no direct control over these bank conflicts. aims to make the expression of this parallelism as simple as possible, while simultaneously enabling operation on CUDA-capable GPUs designed for maximum parallel throughput. FP16 / FP32 Memory Access See Register Pressure. More details about the compute capabilities of various GPUs are in CUDA-Enabled GPUs and Compute Capabilities of the CUDA C++ Programming Guide. Asynchronous and Overlapping Transfers with Computation, 9.2.1.2. Parallelizing these functions as well should increase our speedup potential. The throughput of __sinf(x), __cosf(x), and__expf(x) is much greater than that of sinf(x), cosf(x), and expf(x). Package managers facilitate this process but unexpected issues can still arise and if a bug is found, it necessitates a repeat of the above upgrade process. Adjust kernel launch configuration to maximize device utilization. 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. 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. To use CUDA, data values must be transferred from the host to the device. Fetching ECC bits for each memory transaction also reduced the effective bandwidth by approximately 20% compared to the same GPU with ECC disabled, though the exact impact of ECC on bandwidth can be higher and depends on the memory access pattern. So while the impact is still evident it is not as large as we might have expected. Thread instructions are executed sequentially in CUDA, and, as a result, executing other warps when one warp is paused or stalled is the only way to hide latencies and keep the hardware busy. In these cases, no warp can ever diverge. For example, a matrix multiplication of the same matrices requires N3 operations (multiply-add), so the ratio of operations to elements transferred is O(N), in which case the larger the matrix the greater the performance benefit. In a typical system, thousands of threads are queued up for work (in warps of 32 threads each). For recent versions of CUDA hardware, misaligned data accesses are not a big issue. A useful counterpart to the reference comparisons described above is to structure the code itself in such a way that is readily verifiable at the unit level. For the NVIDIA Tesla V100, global memory accesses with no offset or with offsets that are multiples of 8 words result in four 32-byte transactions. Users wishing to take advantage of such a feature should query its availability with a dynamic check in the code: Alternatively the applications interface might not work at all without a new CUDA driver and then its best to return an error right away: A new error code is added to indicate that the functionality is missing from the driver you are running against: cudaErrorCallRequiresNewerDriver. This is of particular note with loop counters: since it is common for loop counters to have values that are always positive, it may be tempting to declare the counters as unsigned. Indicate whether compute processes can run on the GPU and whether they run exclusively or concurrently with other compute processes. The issue here is the number of operations performed per data element transferred. 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. As mentioned in the PTX section, the compilation of PTX to device code lives along with the CUDA driver, hence the generated PTX might be newer than what is supported by the driver on the deployment system. For global memory accesses, this actual throughput is reported by the Global Load Throughput and Global Store Throughput values. Answer (1 of 2): Shared memory has many more channels(and bandwidth) and works with much less latency. This helps in reducing cache thrashing. 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. Even a relatively slow kernel may be advantageous if it avoids one or more transfers between host and device memory. 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. How do I align things in the following tabular environment? The CUDA Runtime handles kernel loading and setting up kernel parameters and launch configuration before the kernel is launched. Ensure global memory accesses are coalesced. 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. Theoretical bandwidth can be calculated using hardware specifications available in the product literature. As illustrated in Figure 7, non-unit-stride global memory accesses should be avoided whenever possible. To learn more, see our tips on writing great answers. As a result, Thrust can be utilized in rapid prototyping of CUDA applications, where programmer productivity matters most, as well as in production, where robustness and absolute performance are crucial. Testing of all parameters of each product is not necessarily performed by NVIDIA. As described in Asynchronous and Overlapping Transfers with Computation, CUDA streams can be used to overlap kernel execution with data transfers. Low Priority: Use shift operations to avoid expensive division and modulo calculations. 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(). In the next post I will continue our discussion of shared memory by using it to optimize a matrix transpose. The CUDA driver ensures backward Binary Compatibility is maintained for compiled CUDA applications. This is an aggressive optimization that can both reduce numerical accuracy and alter special case handling. Devices of compute capability 1.0 to 1.3 have 16 KB/Block, compute 2.0 onwards have 48 KB/Block shared memory by default. Timeline comparison for copy and kernel execution. When working with a feature exposed in a minor version of the toolkit, the feature might not be available at runtime if the application is running against an older CUDA driver. (The exceptions to this are kernel launches, which return void, and cudaGetErrorString(), which returns a character string describing the cudaError_t code that was passed into it.) The PTX string generated by NVRTC can be loaded by cuModuleLoadData and cuModuleLoadDataEx. To measure performance accurately, it is useful to calculate theoretical and effective bandwidth. An Efficient Matrix Transpose in CUDA C/C++, How to Access Global Memory Efficiently in CUDA C/C++ Kernels, How to Access Global Memory Efficiently in CUDA Fortran Kernels, Top Video Streaming and Conferencing Sessions at NVIDIA GTC 2023, Top Cybersecurity Sessions at NVIDIA GTC 2023, Top Conversational AI Sessions at NVIDIA GTC 2023, Top AI Video Analytics Sessions at NVIDIA GTC 2023, Top Data Science Sessions at NVIDIA GTC 2023. PDF L15: CUDA, cont. Memory Hierarchy and Examples It is therefore best to redistribute the CUDA Runtime library with the application when using dynamic linking or else to statically link against the CUDA Runtime. The compiler can optimize groups of 4 load and store instructions. Once the parallelism of the algorithm has been exposed, it needs to be mapped to the hardware as efficiently as possible. Developers are notified through deprecation and documentation mechanisms of any current or upcoming changes. The host system and the device each have their own distinct attached physical memories 1. Understanding which type of scaling is most applicable to an application is an important part of estimating speedup. By exposing parallelism to the compiler, directives allow the compiler to do the detailed work of mapping the computation onto the parallel architecture. The versions of the components in the toolkit are available in this table. NVIDIA accepts no liability related to any default, damage, costs, or problem which may be based on or attributable to: (i) the use of the NVIDIA product in any manner that is contrary to this document or (ii) customer product designs. The primary differences are in threading model and in separate physical memories: Execution pipelines on host systems can support a limited number of concurrent threads. When an application depends on the availability of certain hardware or software capabilities to enable certain functionality, the CUDA API can be queried for details about the configuration of the available device and for the installed software versions. In other words, the term local in the name does not imply faster access. Weak scaling is a measure of how the time to solution changes as more processors are added to a system with a fixed problem size per processor; i.e., where the overall problem size increases as the number of processors is increased. 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.