Choosing execution parameters is a matter of striking a balance between latency hiding (occupancy) and resource utilization. The maximum number of registers per thread is 255. This helps in reducing cache thrashing. On Wednesday, February 19, 2020, NVIDIA will present part 2 of a 9-part CUDA Training Series titled "CUDA Shared Memory". 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. 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. 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. This action leads to a load of eight L2 cache segments per warp on the Tesla V100 (compute capability 7.0). Shared memory is magnitudes faster to access than global memory. This limitation is not specific to CUDA, but an inherent part of parallel computation on floating-point values. 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. The C++ host code generated by nvcc utilizes the CUDA Runtime, so applications that link to this code will depend on the CUDA Runtime; similarly, any code that uses the cuBLAS, cuFFT, and other CUDA Toolkit libraries will also depend on the CUDA Runtime, which is used internally by these libraries. //Set the attributes to a CUDA stream of type cudaStream_t, Mapping Persistent data accesses to set-aside L2 in sliding window experiment, /*Each CUDA thread accesses one element in the persistent data section. The warp size is 32 threads and the number of banks is also 32, so bank conflicts can occur between any threads in the warp. The effective bandwidth of this routine is 195.5 GB/s on an NVIDIA Tesla V100. It will not allow any other CUDA call to begin until it has completed.) 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. 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. A CUDA device has a number of different memory components that are available to programmers - register, shared memory, local memory, global memory and constant memory. cudaDeviceSynchronize()blocks the calling CPU thread until all CUDA calls previously issued by the thread are completed. Recommendations for taking advantage of minor version compatibility in your application, 16.4. More details about the compute capabilities of various GPUs are in CUDA-Enabled GPUs and Compute Capabilities of the CUDA C++ Programming Guide. Inspection of the PTX assembly code (obtained by compiling with -ptx or -keep command-line options to nvcc) reveals whether a variable has been placed in local memory during the first compilation phases. For example, consider the following code: Here, the sub-expression stride*i could overflow a 32-bit integer, so if i is declared as unsigned, the overflow semantics prevent the compiler from using some optimizations that might otherwise have applied, such as strength reduction. Intermediate data structures should be created in device memory, operated on by the device, and destroyed without ever being mapped by the host or copied to host memory. There are several key strategies for parallelizing sequential code. Formulae for exponentiation by small fractions, Sample CUDA configuration data reported by deviceQuery, +-----------------------------------------------------------------------------+, |-------------------------------+----------------------+----------------------+, |===============================+======================+======================|, +-------------------------------+----------------------+----------------------+, |=============================================================================|, cudaDevAttrCanUseHostPointerForRegisteredMem, 1.3. But this technique is still useful for other access patterns, as Ill show in the next post.). As mentioned in Occupancy, higher occupancy does not always equate to better performance. Copyright 2007-2023, NVIDIA Corporation & Affiliates. CUDA applications are built against the CUDA Runtime library, which handles device, memory, and kernel management. Alternatively, NVIDIA provides an occupancy calculator in the form of an Excel spreadsheet that enables developers to hone in on the optimal balance and to test different possible scenarios more easily. 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. Application binaries rely on CUDA Driver API interface and even though the CUDA Driver API itself may also have changed across toolkit versions, CUDA guarantees Binary Compatibility of the CUDA Driver API interface. GPUs with compute capability 8.6 support shared memory capacity of 0, 8, 16, 32, 64 or 100 KB per SM. If we validate our addressing logic separately prior to introducing the bulk of the computation, then this will simplify any later debugging efforts. 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. As a useful side effect, this strategy will allow us a means to reduce code duplication should we wish to include both CPU and GPU execution paths in our application: if the bulk of the work of our CUDA kernels is done in __host__ __device__ functions, we can easily call those functions from both the host code and the device code without duplication. The last argument to the cudaMemcpyAsync() function is the stream ID, which in this case uses the default stream, stream 0. 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. Devices of compute capability 8.6 have 2x more FP32 operations per cycle per SM than devices of compute capability 8.0. I have locally sorted queues in different blocks of cuda. These instructions also avoid using extra registers for memory copies and can also bypass the L1 cache. It would have been more so if adjacent warps had not exhibited such a high degree of reuse of the over-fetched cache lines. 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. Code samples throughout the guide omit error checking for conciseness. Because shared memory is shared by threads in a thread block, it provides a mechanism for threads to cooperate. To analyze performance, it is necessary to consider how warps access global memory in the for loop. For more details refer to the L2 Access Management section in the CUDA C++ Programming Guide. Some metric related to the number of active warps on a multiprocessor is therefore important in determining how effectively the hardware is kept busy. 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. Aside from memory bank conflicts, there is no penalty for non-sequential or unaligned accesses by a warp in shared memory. However, this latency can be completely hidden by the execution of threads in other warps. On Linux systems, the CUDA driver and kernel mode components are delivered together in the NVIDIA display driver package. We adjust the copy_count in the kernels such that each thread block copies from 512 bytes up to 48 MB. There's no way around this. 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. Shared memory is extremely fast, user managed, on-chip memory that can be used to share data between threads within a thread block. When a CUDA kernel accesses a data region in the global memory repeatedly, such data accesses can be considered to be persisting. 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. 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. When linking with dynamic libraries from the toolkit, the library must be equal to or newer than what is needed by any one of the components involved in the linking of your application. if several threads had accessed the same word or if some threads did not participate in the access), the full segment is fetched anyway. Instead of a __syncthreads()synchronization barrier call, a __syncwarp() is sufficient after reading the tile of A into shared memory because only threads within the warp that write the data into shared memory read this data. Mapping Persistent data accesses to set-aside L2 in sliding window experiment. To use other CUDA APIs introduced in a minor release (that require a new driver), one would have to implement fallbacks or fail gracefully. However, if multiple threads requested addresses map to the same memory bank, the accesses are serialized. Optimal global memory coalescing is achieved for both reads and writes because global memory is always accessed through the linear, aligned index t. The reversed index tr is only used to access shared memory, which does not have the sequential access restrictions of global memory for optimal performance. High Priority: To maximize developer productivity, profile the application to determine hotspots and bottlenecks. Threads on a CPU are generally heavyweight entities. CUDA Memory Rules Currently can only transfer data from host to global (and constant memory) and not host directly to shared. math libraries or deep learning frameworks) do not have a direct dependency on the CUDA runtime, compiler or driver. In the example above, we can clearly see that the function genTimeStep() takes one-third of the total running time of the application. In Using shared memory to improve the global memory load efficiency in matrix multiplication, each element in a tile of A is read from global memory only once, in a fully coalesced fashion (with no wasted bandwidth), to shared memory. 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. A place where magic is studied and practiced? See Math Libraries. Non-default streams are required for this overlap because memory copy, memory set functions, and kernel calls that use the default stream begin only after all preceding calls on the device (in any stream) have completed, and no operation on the device (in any stream) commences until they are finished. Existing CUDA Applications within Minor Versions of CUDA, 15.4.1.1. Loop Counters Signed vs. Unsigned, 11.1.5. The NVIDIA System Management Interface (nvidia-smi) is a command line utility that aids in the management and monitoring of NVIDIA GPU devices. The easiest option is to statically link against the CUDA Runtime. It allows developers to use a CUDA-enabled graphics processing unit (GPU) to accelerate processing tasks in their applications. For more information please refer to the section on Async Copy in the CUDA C++ Programming Guide. 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. The -use_fast_math compiler option of nvcc coerces every functionName() call to the equivalent __functionName() call. 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. Strong Scaling and Amdahls Law describes strong scaling, which allows us to set an upper bound for the speedup with a fixed problem size. Avoid long sequences of diverged execution by threads within the same warp. Resources stay allocated to each thread until it completes its execution. Recall that the initial assess step allowed the developer to determine an upper bound for the potential speedup attainable by accelerating given hotspots. However, if multiple addresses of a memory request map to the same memory bank, the accesses are serialized. The most important consideration with any profiling activity is to ensure that the workload is realistic - i.e., that information gained from the test and decisions based upon that information are relevant to real data. If such an application is run on a system with the R418 driver installed, CUDA initialization will return an error as can be seen in the example below. 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. The NVIDIA Ampere GPU architecture increases the capacity of the L2 cache to 40 MB in Tesla A100, which is 7x larger than Tesla V100. The NVIDIA Ampere GPU architecture adds hardware acceleration for a split arrive/wait barrier in shared memory. 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. The cudaChooseDevice() function can be used to select the device that most closely matches a desired set of features. Bandwidth - the rate at which data can be transferred - is one of the most important gating factors for performance. Sometimes, the compiler may unroll loops or optimize out if or switch statements by using branch predication instead. However, the set of registers (known as the register file) is a limited commodity that all threads resident on a multiprocessor must share. Does there exist a square root of Euler-Lagrange equations of a field? Note that Gustafsons Law assumes that the ratio of serial to parallel execution remains constant, reflecting additional cost in setting up and handling the larger problem. In the asynchronous version of the kernel, instructions to load from global memory and store directly into shared memory are issued as soon as __pipeline_memcpy_async() function is called. 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. Going a step further, if most functions are defined as __host__ __device__ rather than just __device__ functions, then these functions can be tested on both the CPU and the GPU, thereby increasing our confidence that the function is correct and that there will not be any unexpected differences in the results. Between 128 and 256 threads per block is a good initial range for experimentation with different block sizes. We define source compatibility as a set of guarantees provided by the library, where a well-formed application built against a specific version of the library (using the SDK) will continue to build and run without errors when a newer version of the SDK is installed. The amount of performance benefit an application will realize by running on CUDA depends entirely on the extent to which it can be parallelized. Asynchronous Copy from Global Memory to Shared Memory, 10. Best performance with synchronous copy is achieved when the copy_count parameter is a multiple of 4 for all three element sizes. The NVIDIA A100 GPU based on compute capability 8.0 increases the maximum capacity of the combined L1 cache, texture cache and shared memory to 192 KB, 50% larger than the L1 cache in NVIDIA V100 GPU. This new feature is exposed via the pipeline API in CUDA. All threads within one block see the same shared memory array . The same goes for other CUDA Toolkit libraries: cuFFT has an interface similar to that of FFTW, etc. Having understood the application profile, the developer should understand how the problem size would change if the computational performance changes and then apply either Amdahls or Gustafsons Law to determine an upper bound for the speedup.