cuda shared memory between blocks cuda shared memory between blocks

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). The remainder of the kernel code is identical to the staticReverse() kernel. A natural decomposition of the problem is to use a block and tile size of wxw threads. 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. From CUDA 11.3 NVRTC is also semantically versioned. I have locally sorted queues in different blocks of cuda. Global memory: is the memory residing graphics/accelerator card but not inside GPU chip. It will not allow any other CUDA call to begin until it has completed.) Best performance with synchronous copy is achieved when the copy_count parameter is a multiple of 4 for all three element sizes. The access policy window requires a value for hitRatio and num_bytes. 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. The NVIDIA nvcc compiler driver converts .cu files into C++ for the host system and CUDA assembly or binary instructions for the device. These include threading issues, unexpected values due to the way floating-point values are computed, and challenges arising from differences in the way CPU and GPU processors operate. Comparing Synchronous vs Asynchronous Copy from Global Memory to Shared Memory. Its like a local cache shared among the threads of a block. How to time code using CUDA events illustrates their use. On GPUs with GDDR memory with ECC enabled the available DRAM is reduced by 6.25% to allow for the storage of ECC bits. The performance guidelines and best practices described in the CUDA C++ Programming Guide and the CUDA C++ Best Practices Guide apply to all CUDA-capable GPU architectures. For example, in the standard CUDA Toolkit installation, the files libcublas.so and libcublas.so.5.5 are both symlinks pointing to a specific build of cuBLAS, which is named like libcublas.so.5.5.x, where x is the build number (e.g., libcublas.so.5.5.17). For each iteration i of the for loop, the threads in a warp read a row of the B tile, which is a sequential and coalesced access for all compute capabilities. Therefore, the compiler can optimize more aggressively with signed arithmetic than it can with unsigned arithmetic. (This was the default and only option provided in CUDA versions 5.0 and earlier.). The NVIDIA Ampere GPU architecture adds hardware acceleration for copying data from global memory to shared memory. The NVIDIA Nsight Visual Studio Edition for Microsoft Windows 7, Windows HPC Server 2008, Windows 8.1, and Windows 10 is available as a free plugin for Microsoft Visual Studio; see: https://developer.nvidia.com/nsight-visual-studio-edition. (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.) It may be different with non-unit-strided accesses, however, and this is a pattern that occurs frequently when dealing with multidimensional data or matrices. For a listing of some of these tools, see https://developer.nvidia.com/cluster-management. 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. CUDA shared memory not faster than global? Aside from memory bank conflicts, there is no penalty for non-sequential or unaligned accesses by a warp in shared memory. Reproduction of information in this document is permissible only if approved in advance by NVIDIA in writing, reproduced without alteration and in full compliance with all applicable export laws and regulations, and accompanied by all associated conditions, limitations, and notices. CUDA Compatibility Across Minor Releases, 15.4.1. Choosing execution parameters is a matter of striking a balance between latency hiding (occupancy) and resource utilization. Multiple kernels executing at the same time is known as concurrent kernel execution. The functions that make up the CUDA Runtime API are explained in the CUDA Toolkit Reference Manual. The effective bandwidth for this kernel is 12.8 GB/s on an NVIDIA Tesla V100. Between 128 and 256 threads per block is a good initial range for experimentation with different block sizes. Threads copy the data from global memory to shared memory with the statement s[t] = d[t], and the reversal is done two lines later with the statement d[t] = s[tr]. An explicit __syncwarp() can be used to guarantee that the warp has reconverged for subsequent instructions. This is an aggressive optimization that can both reduce numerical accuracy and alter special case handling. 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. Shared memory is extremely fast, user managed, on-chip memory that can be used to share data between threads within a thread block. Likewise, for exponentation with an exponent of -1/3, use rcbrt() or rcbrtf(). Hence, access to local memory is as expensive as access to global memory. 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(). The compiler and hardware thread scheduler will schedule instructions as optimally as possible to avoid register memory bank conflicts. 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. Applications composed with these differences in mind can treat the host and device together as a cohesive heterogeneous system wherein each processing unit is leveraged to do the kind of work it does best: sequential work on the host and parallel work on the device. Host memory allocations pinned after-the-fact via cudaHostRegister(), however, will continue to have different device pointers than their host pointers, so cudaHostGetDevicePointer() remains necessary in that case. When deploying a CUDA application, it is often desirable to ensure that the application will continue to function properly even if the target machine does not have a CUDA-capable GPU and/or a sufficient version of the NVIDIA Driver installed. (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.). It is recommended to use cudaMallocAsync() and cudaFreeAsync() which are stream ordered pool allocators to manage device memory. 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). A variant of the previous matrix multiplication can be used to illustrate how strided accesses to global memory, as well as shared memory bank conflicts, are handled. Both the CUDA driver and the CUDA runtime are not source compatible across the different SDK releases. For example, to compute the effective bandwidth of a 2048 x 2048 matrix copy, the following formula could be used: \(\text{Effective\ bandwidth} = \left( {\left( 2048^{2} \times 4 \times 2 \right) \div 10^{9}} \right) \div \text{time}\). Mapping Persistent data accesses to set-aside L2 in sliding window experiment. Conversion to Warp isn't possible for Week 6-7 because there is no support for shared memory or block level synchronization. The issue here is the number of operations performed per data element transferred. 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. Effective bandwidth is calculated by timing specific program activities and by knowing how data is accessed by the program. 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. Access to shared memory is much faster than global memory access because it is located on a chip. Some aspects of this behavior such as cache location and maximum cache size can be controlled via the use of environment variables; see Just in Time Compilation of the CUDA C++ Programming Guide. The -use_fast_math compiler option of nvcc coerces every functionName() call to the equivalent __functionName() call. Its important to note that both numbers are useful. If cudaGetDeviceCount() reports an error, the application should fall back to an alternative code path. The NVIDIA Ampere GPU architecture adds native support for warp wide reduction operations for 32-bit signed and unsigned integer operands. To keep the kernels simple, M and N are multiples of 32, since the warp size (w) is 32 for current devices. Results obtained using double-precision arithmetic will frequently differ from the same operation performed via single-precision arithmetic due to the greater precision of the former and due to rounding issues. It is important to include the overhead of transferring data to and from the device in determining whether operations should be performed on the host or on the device. 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. Missing dependencies is also a binary compatibility break, hence you should provide fallbacks or guards for functionality that depends on those interfaces. Consequently, its important to understand the characteristics of the architecture. This makes the code run faster at the cost of diminished precision and accuracy. On all CUDA-enabled devices, it is possible to overlap host computation with asynchronous data transfers and with device computations. In such cases, users or developers can still benefit from not having to upgrade the entire CUDA Toolkit or driver to use these libraries or frameworks. Single-precision floats provide the best performance, and their use is highly encouraged. The CUDA compiler (nvcc), provides a way to handle CUDA and non-CUDA code (by splitting and steering compilation), along with the CUDA runtime, is part of the CUDA compiler toolchain. Increased L2 capacity and L2 Residency Controls, 1.4.2.3. Shared memory accesses, in counterpoint, are usually worth optimizing only when there exists a high degree of bank conflicts. What if you need multiple dynamically sized arrays in a single kernel? Weak scaling is often equated with Gustafsons Law, which states that in practice, the problem size scales with the number of processors. Current utilization rates are reported for both the compute resources of the GPU and the memory interface. The read-only texture memory space is cached. Since some CUDA API calls and all kernel launches are asynchronous with respect to the host code, errors may be reported to the host asynchronously as well; often this occurs the next time the host and device synchronize with each other, such as during a call to cudaMemcpy() or to cudaDeviceSynchronize(). (Factorization). For purposes of calculating occupancy, the number of registers used by each thread is one of the key factors. A portion of the L2 cache can be set aside for persistent accesses to a data region in global memory. A subset of CUDA APIs dont need a new driver and they can all be used without any driver dependencies. Recall that shared memory is local to each SM. This microbenchmark uses a 1024 MB region in GPU global memory. If textures are fetched using tex1D(),tex2D(), or tex3D() rather than tex1Dfetch(), the hardware provides other capabilities that might be useful for some applications such as image processing, as shown in Table 4. 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. Last updated on Feb 27, 2023. cudaFuncAttributePreferredSharedMemoryCarveout, 1. Dont expose ABI structures that can change. Because it is on-chip, shared memory is much faster than local and global memory. Asynchronous transfers enable overlap of data transfers with computation in two different ways. 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). A shared memory request for a warp is split into one request for the first half of the warp and one request for the second half of the warp. (tens of kBs capacity) Global memory is main memory (GDDR,HBM, (1-32 GB)) and data is cached by L2,L1 caches. On devices with GDDR memory, accessing memory in a coalesced way is even more important when ECC is turned on. 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. This makes the code run faster at the cost of diminished precision and accuracy. Such a pattern is shown in Figure 3. Asynchronous and Overlapping Transfers with Computation, 9.2.1.2. This is particularly beneficial to kernels that frequently call __syncthreads(). Cached in L1 and L2 by default except on devices of compute capability 5.x; devices of compute capability 5.x cache locals only in L2. sm_80) rather than a virtual architecture (e.g. 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. Instructions with a false predicate do not write results, and they also do not evaluate addresses or read operands. Copyright 2007-2023, NVIDIA Corporation & Affiliates. 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. Of these different memory spaces, global memory is the most plentiful; see Features and Technical Specifications of the CUDA C++ Programming Guide for the amounts of memory available in each memory space at each compute capability level. To do so, use this equation: \(\text{Effective\ bandwidth} = \left( {\left( B_{r} + B_{w} \right) \div 10^{9}} \right) \div \text{time}\). and one element in the streaming data section. A slightly related but important topic is one of application binary compatibility across GPU architectures in CUDA. For recent versions of CUDA hardware, misaligned data accesses are not a big issue. It also avoids an intermediary register file access traditionally present between the global memory read and the shared memory write. It is limited. There are multiple ways to declare shared memory inside a kernel, depending on whether the amount of memory is known at compile time or at run time. 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. This cost has several ramifications: The complexity of operations should justify the cost of moving data to and from the device. 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. The NVIDIA A100 GPU supports shared memory capacity of 0, 8, 16, 32, 64, 100, 132 or 164 KB per SM. To get a closer match between values, set the x86 host processor to use regular double or single precision (64 bits and 32 bits, respectively). Along with the increased memory capacity, the bandwidth is increased by 72%, from 900 GB/s on Volta V100 to 1550 GB/s on A100. When the persistent data region fits well into the 30 MB set-aside portion of the L2 cache, a performance increase of as much as 50% is observed. 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. These are the same contexts used implicitly by the CUDA Runtime when there is not already a current context for a thread. The remaining portion of this persistent data will be accessed using the streaming property. If the PTX is also not available, then the kernel launch will fail. To measure performance accurately, it is useful to calculate theoretical and effective bandwidth. CUDA reserves 1 KB of shared memory per thread block. The examples in this section have illustrated three reasons to use shared memory: To enable coalesced accesses to global memory, especially to avoid large strides (for general matrices, strides are much larger than 32), To eliminate (or reduce) redundant loads from global memory. To execute code on devices of specific compute capability, an application must load binary or PTX code that is compatible with this compute capability. Programmers should be aware of two version numbers. 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. (To determine the latter number, see the deviceQuery CUDA Sample or refer to Compute Capabilities in the CUDA C++ Programming Guide.) For some fractional exponents, exponentiation can be accelerated significantly compared to the use of pow() by using square roots, cube roots, and their inverses. GPUs with compute capability 8.6 support shared memory capacity of 0, 8, 16, 32, 64 or 100 KB per SM. The cudaDeviceEnablePeerAccess() API call remains necessary to enable direct transfers (over either PCIe or NVLink) between GPUs. 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. Another benefit of its union with shared memory, similar to Volta L1 is improvement in terms of both latency and bandwidth. This means that even though an application source might need to be changed if it has to be recompiled against a newer CUDA Toolkit in order to use the newer features, replacing the driver components installed in a system with a newer version will always support existing applications and its functions. Notwithstanding any damages that customer might incur for any reason whatsoever, NVIDIAs aggregate and cumulative liability towards customer for the products described herein shall be limited in accordance with the Terms of Sale for the product. Finally, higher bandwidth between the host and the device is achieved when using page-locked (or pinned) memory, as discussed in the CUDA C++ Programming Guide and the Pinned Memory section of this document. Strong Scaling and Amdahls Law, 3.1.3.2. The core computational unit, which includes control, arithmetic, registers and typically some cache, is replicated some number of times and connected to memory via a network.