A place where magic is studied and practiced? Throughout this guide, specific recommendations are made regarding the design and implementation of CUDA C++ code. This is evident from the saw tooth curves. For single-precision code, use of the float type and the single-precision math functions are highly recommended. Always check the error return values on all CUDA API functions, even for functions that are not expected to fail, as this will allow the application to detect and recover from errors as soon as possible should they occur. 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. Instead, strategies can be applied incrementally as they are learned. CUDA C++ provides a simple path for users familiar with the C++ programming language to easily write programs for execution by the device. Memory Access As a result, a read from constant memory costs one memory read from device memory only on a cache miss; otherwise, it just costs one read from the constant cache. 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. While NVIDIA GPUs are frequently associated with graphics, they are also powerful arithmetic engines capable of running thousands of lightweight threads in parallel. Memory optimizations are the most important area for performance. Another important concept is the management of system resources allocated for a particular task. On devices that are capable of concurrent copy and compute, it is possible to overlap kernel execution on the device with data transfers between the host and the device. When accessing uncached local or global memory, there are hundreds of clock cycles of memory latency. By using new CUDA versions, users can benefit from new CUDA programming model APIs, compiler optimizations and math library features. Consider a simple transpose of a [2048, 1024] matrix to [1024, 2048]. 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. The NVIDIA Ampere GPU architecture is NVIDIAs latest architecture for CUDA compute applications. For more information please refer to the section on Async Copy in the CUDA C++ Programming Guide. Finally, particular attention must be paid to control flow instructions due to the SIMT (single instruction multiple thread) nature of the device. If it has not, subsequent compilation phases might still decide otherwise, if they find the variable consumes too much register space for the targeted architecture. This section examines the functionality, advantages, and pitfalls of both approaches. Since you don't indicate where your "locally sorted" data resides, this could indicate a copying of that much data at least (for example, if they are locally sorted and reside in shared memory). (See Data Transfer Between Host and Device.) What sort of strategies would a medieval military use against a fantasy giant? Shared memory is allocated per thread block, so all threads in the block have access to the same shared memory. A shared memory request for a warp is not split as with devices of compute capability 1.x, meaning that bank conflicts can occur between threads in the first half of a warp and threads in the second half of the same warp. The NVIDIA A100 GPU supports shared memory capacity of 0, 8, 16, 32, 64, 100, 132 or 164 KB per SM. In this code, the canMapHostMemory field of the structure returned by cudaGetDeviceProperties() is used to check that the device supports mapping host memory to the devices address space. The cudaDeviceCanAccessPeer() can be used to determine if peer access is possible between any pair of GPUs. \times (4096/8) \times 2 \right) \div 10^{9} = 898\text{GB/s}\). However, if multiple threads requested addresses map to the same memory bank, the accesses are serialized. 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. 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. High Priority: Use the effective bandwidth of your computation as a metric when measuring performance and optimization benefits. Using the CUDA Occupancy Calculator to project GPU multiprocessor occupancy. These instructions also avoid using extra registers for memory copies and can also bypass the L1 cache. The first is the compute capability, and the second is the version number of the CUDA Runtime and CUDA Driver APIs. In the NVIDIA Ampere GPU architecture, the portion of the L1 cache dedicated to shared memory (known as the carveout) can be selected at runtime as in previous architectures such as Volta, using cudaFuncSetAttribute() with the attribute cudaFuncAttributePreferredSharedMemoryCarveout. (BFloat16 only supports FP32 as accumulator), unsigned char/signed char (8-bit precision). Because the size of the persistent memory region (freqSize * sizeof(int) bytes) is much, smaller than the size of the streaming memory region (dataSize * sizeof(int) bytes), data, in the persistent region is accessed more frequently*/, //Number of bytes for persisting accesses in range 10-60 MB, //Hint for cache hit ratio. This approach will tend to provide the best results for the time invested and will avoid the trap of premature optimization. This document is provided for information purposes only and shall not be regarded as a warranty of a certain functionality, condition, or quality of a product. Applications compiled against a CUDA Toolkit version will only run on systems with the specified minimum driver version for that toolkit version. 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. Choosing the execution configuration parameters should be done in tandem; however, there are certain heuristics that apply to each parameter individually. 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. When using multiple GPUs from the same application, it is recommended to use GPUs of the same type, rather than mixing hardware generations. 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. The effective bandwidth can vary by an order of magnitude depending on the access pattern for each type of memory. Within each iteration of the for loop, a value in shared memory is broadcast to all threads in a warp. 1 Answer Sorted by: 2 You don't need to worry about this. The maximum number of concurrent warps per SM remains the same as in Volta (i.e., 64), and other factors influencing warp occupancy are: The register file size is 64K 32-bit registers per SM. 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. A pointer to a structure with a size embedded is a better solution. Access to shared memory is much faster than global memory access because it is located on chip. (This was the default and only option provided in CUDA versions 5.0 and earlier.). CUDA Binary (cubin) Compatibility, 15.4. For example, the ability to overlap kernel execution with asynchronous data transfers between the host and the device is available on most but not all GPUs irrespective of the compute capability. Information published by NVIDIA regarding third-party products or services does not constitute a license from NVIDIA to use such products or services or a warranty or endorsement thereof. By understanding how applications can scale it is possible to set expectations and plan an incremental parallelization strategy. The amount of performance benefit an application will realize by running on CUDA depends entirely on the extent to which it can be parallelized. Having a semantically versioned ABI means the interfaces need to be maintained and versioned. 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. As even CPU architectures require exposing this parallelism in order to improve or simply maintain the performance of sequential applications, the CUDA family of parallel programming languages (CUDA C++, CUDA Fortran, etc.) Both of your questions imply some sort of global synchronization. The value of this field is propagated into an application built against the library and is used to locate the library of the correct version at runtime. To learn more, see our tips on writing great answers. The synchronous version for the kernel loads an element from global memory to an intermediate register and then stores the intermediate register value to shared memory. For example, the NVIDIA Tesla V100 uses HBM2 (double data rate) RAM with a memory clock rate of 877 MHz and a 4096-bit-wide memory interface. 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 most straightforward approach to parallelizing an application is to leverage existing libraries that take advantage of parallel architectures on our behalf. Instead, all instructions are scheduled, but a per-thread condition code or predicate controls which threads execute the instructions. It then explores how bandwidth affects performance metrics and how to mitigate some of the challenges it poses. This chapter discusses how to correctly measure performance using CPU timers and CUDA events. On parallel systems, it is possible to run into difficulties not typically found in traditional serial-oriented programming. A stride of 2 results in a 50% of load/store efficiency since half the elements in the transaction are not used and represent wasted bandwidth. Moreover, in such cases, the argument-reduction code uses local memory, which can affect performance even more because of the high latency of local memory. For more details refer to the memcpy_async section in the CUDA C++ Programming Guide. The read-only texture memory space is cached. For example, if the install name of the cuBLAS library is given as @rpath/libcublas.5.5.dylib, then the library is version 5.5 and the copy of this library redistributed with the application must be named libcublas.5.5.dylib, even though only -lcublas (with no version number specified) is used at link time. Although it is also possible to synchronize the CPU thread with a particular stream or event on the GPU, these synchronization functions are not suitable for timing code in streams other than the default stream. Before I show you how to avoid striding through global memory in the next post, first I need to describe shared memory in some detail. 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. We can then launch this kernel onto the GPU and retrieve the results without requiring major rewrites to the rest of our application. We will note some of them later on in the document. It enables GPU threads to directly access host memory. After this change, the effective bandwidth is 199.4 GB/s on an NVIDIA Tesla V100, which is comparable to the results from the last C = AB kernel. The cubins are architecture-specific. In A copy kernel that illustrates misaligned accesses, data is copied from the input array idata to the output array, both of which exist in global memory. Devices of compute capability 8.6 have 2x more FP32 operations per cycle per SM than devices of compute capability 8.0. When we can, we should use registers. To analyze performance, it is necessary to consider how warps access global memory in the for loop. Shared memory is a CUDA memory space that is shared by all threads in a thread block. 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. 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. For codes continuing to make use of PTX, in order to support compiling on an older driver, your code must be first transformed into device code via the static ptxjitcompiler library or NVRTC with the option of generating code for a specific architecture (e.g. Another benefit of its union with shared memory, similar to Volta L1 is improvement in terms of both latency and bandwidth. The constant memory space is cached. The Perl bindings are provided via CPAN and the Python bindings via PyPI. 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. First, we set aside 30 MB of the L2 cache for persisting accesses using cudaDeviceSetLimit(), as discussed above. Furthermore, the need for context switching can reduce utilization when work from several contexts could otherwise execute concurrently (see also Concurrent Kernel Execution). Understanding which type of scaling is most applicable to an application is an important part of estimating speedup. Single-precision floats provide the best performance, and their use is highly encouraged. (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. Note that the NVIDIA Tesla A100 GPU has 40 MB of total L2 cache capacity. The following examples use the cuBLAS library from CUDA Toolkit 5.5 as an illustration: In a shared library on Linux, there is a string field called the SONAME that indicates the binary compatibility level of the library. This kernel has an effective bandwidth of 144.4 GB/s on an NVIDIA Tesla V100. Coalesced using shared memory to store a tile of A, Using shared memory to eliminate redundant reads of a tile of B. (In Staged concurrent copy and execute, it is assumed that N is evenly divisible by nThreads*nStreams.) We cannot declare these directly, but small static allocations go . From CUDA 11.3 NVRTC is also semantically versioned. The NVIDIA System Management Interface (nvidia-smi) is a command line utility that aids in the management and monitoring of NVIDIA GPU devices. Mutually exclusive execution using std::atomic? Support for TF32 Tensor Core, through HMMA instructions. 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. Lets assume that A and B are threads in two different warps. Formulae for exponentiation by small fractions, Sample CUDA configuration data reported by deviceQuery, +-----------------------------------------------------------------------------+, |-------------------------------+----------------------+----------------------+, |===============================+======================+======================|, +-------------------------------+----------------------+----------------------+, |=============================================================================|, cudaDevAttrCanUseHostPointerForRegisteredMem, 1.3. However, striding through global memory is problematic regardless of the generation of the CUDA hardware, and would seem to be unavoidable in many cases, such as when accessing elements in a multidimensional array along the second and higher dimensions. Because of these nuances in register allocation and the fact that a multiprocessors shared memory is also partitioned between resident thread blocks, the exact relationship between register usage and occupancy can be difficult to determine. Bandwidth - the rate at which data can be transferred - is one of the most important gating factors for performance. In the case of texture access, if a texture reference is bound to a linear array in global memory, then the device code can write to the underlying array. If instead i is declared as signed, where the overflow semantics are undefined, the compiler has more leeway to use these optimizations. The example below shows how an existing example can be adapted to use the new features, guarded by the USE_CUBIN macro in this case: We recommend that the CUDA runtime be statically linked to minimize dependencies. 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. The output for that program is shown in Figure 16. To keep the kernels simple, M and N are multiples of 32, since the warp size (w) is 32 for current devices. If this set-aside portion is not used by persistent accesses, then streaming or normal data accesses can use it. Providing the two argument version of __launch_bounds__(maxThreadsPerBlock,minBlocksPerMultiprocessor) can improve performance in some cases. Similarly, the single-precision functions sinpif(), cospif(), and sincospif() should replace calls to sinf(), cosf(), and sincosf() when the function argument is of the form *. Block-column matrix multiplied by block-row matrix. This guide introduces the Assess, Parallelize, Optimize, Deploy(APOD) design cycle for applications with the goal of helping application developers to rapidly identify the portions of their code that would most readily benefit from GPU acceleration, rapidly realize that benefit, and begin leveraging the resulting speedups in production as early as possible. 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. The actual memory throughput shows how close the code is to the hardware limit, and a comparison of the effective or requested bandwidth to the actual bandwidth presents a good estimate of how much bandwidth is wasted by suboptimal coalescing of memory accesses (see Coalesced Access to Global Memory). At a minimum, you would need some sort of selection process that can access the heads of each queue. Lets say that two threads A and B each load a data element from global memory and store it to shared memory. 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.