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The compiler will perform these conversions if n is literal. As with the previous section on library building recommendations, if using the CUDA runtime, we recommend linking to the CUDA runtime statically when building your application. The CUDA Driver API thus is binary-compatible (the OS loader can pick up a newer version and the application continues to work) but not source-compatible (rebuilding your application against a newer SDK might require source changes). As described in Asynchronous and Overlapping Transfers with Computation, CUDA streams can be used to overlap kernel execution with data transfers. 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. Context switches (when two threads are swapped) are therefore slow and expensive. As a particular example, to evaluate the sine function in degrees instead of radians, use sinpi(x/180.0). 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. It allows developers to use a CUDA-enabled graphics processing unit (GPU) to accelerate processing tasks in their applications. 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. These results should be compared with those in Table 2. HBM2 memories, on the other hand, provide dedicated ECC resources, allowing overhead-free ECC protection.2. OpenCL is a trademark of Apple Inc. used under license to the Khronos Group Inc. NVIDIA and the NVIDIA logo are trademarks or registered trademarks of NVIDIA Corporation in the U.S. and other countries. However, it is best to avoid accessing global memory whenever possible. The -use_fast_math compiler option of nvcc coerces every functionName() call to the equivalent __functionName() call. For example, many kernels have complex addressing logic for accessing memory in addition to their actual computation. Strong scaling is usually equated with Amdahls Law, which specifies the maximum speedup that can be expected by parallelizing portions of a serial program. To analyze performance, it is necessary to consider how warps access global memory in the for loop. CUDA devices use several memory spaces, which have different characteristics that reflect their distinct usages in CUDA applications. A simple implementation for C = AAT is shown in Unoptimized handling of strided accesses to global memory, Unoptimized handling of strided accesses to global memory. A key aspect of correctness verification for modifications to any existing program is to establish some mechanism whereby previous known-good reference outputs from representative inputs can be compared to new results. "After the incident", I started to be more careful not to trip over things. Constant memory used for data that does not change (i.e. Such a pattern is shown in Figure 3. This chapter contains a summary of the recommendations for optimization that are explained in this document. Ensure global memory accesses are coalesced. Devices of compute capability 2.0 and later support a special addressing mode called Unified Virtual Addressing (UVA) on 64-bit Linux and Windows. It will not allow any other CUDA call to begin until it has completed.) With UVA, the host memory and the device memories of all installed supported devices share a single virtual address space. This chapter discusses how to correctly measure performance using CPU timers and CUDA events. For the latter variety of application, some degree of code refactoring to expose the inherent parallelism in the application might be necessary, but keep in mind that this refactoring work will tend to benefit all future architectures, CPU and GPU alike, so it is well worth the effort should it become necessary. 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). NVIDIA makes no representation or warranty that products based on this document will be suitable for any specified use. Pinned memory is allocated using the cudaHostAlloc() functions in the Runtime API. 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. Shared memory is allocated per thread block, so all threads in the block have access to the same shared memory. But this technique is still useful for other access patterns, as Ill show in the next post.). cudart 11.1 is statically linked) is run on the system, we see that it runs successfully even when the driver reports a 11.0 version - that is, without requiring the driver or other toolkit components to be updated on the system. Not all threads need to participate. Once the parallelism of the algorithm has been exposed, it needs to be mapped to the hardware as efficiently as possible. The following sections explain the principal items of interest. Single-precision floats provide the best performance, and their use is highly encouraged. I have locally sorted queues in different blocks of cuda. 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. Unified Shared Memory/L1/Texture Cache, NVIDIA Ampere GPU Architecture Compatibility Guide for CUDA Applications. In the kernel launch, specify the total shared memory needed, as in the following. Zero copy can be used in place of streams because kernel-originated data transfers automatically overlap kernel execution without the overhead of setting up and determining the optimal number of streams. Applications already using other BLAS libraries can often quite easily switch to cuBLAS, for example, whereas applications that do little to no linear algebra will have little use for cuBLAS. Furthermore, if accesses by the threads of the warp had been permuted within or accross the four segments, still only four 32-byte transactions would have been performed by a device with compute capability 6.0 or higher. My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? They can be distinguished by their names: some have names with prepended underscores, whereas others do not (e.g., __functionName() versus functionName()). Last updated on Feb 27, 2023. cudaFuncAttributePreferredSharedMemoryCarveout, 1. In reality, most applications do not exhibit perfectly linear strong scaling, even if they do exhibit some degree of strong scaling. Do new devs get fired if they can't solve a certain bug? Under UVA, pinned host memory allocated with cudaHostAlloc() will have identical host and device pointers, so it is not necessary to call cudaHostGetDevicePointer() for such allocations. The following throughput metrics can be displayed in the Details or Detail Graphs view: The Requested Global Load Throughput and Requested Global Store Throughput values indicate the global memory throughput requested by the kernel and therefore correspond to the effective bandwidth obtained by the calculation shown under Effective Bandwidth Calculation. Dynamic parallelism - passing contents of shared memory to spawned blocks? 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. Because shared memory is shared by threads in a thread block, it provides a mechanism for threads to cooperate. The CUDA Toolkits End-User License Agreement (EULA) allows for redistribution of many of the CUDA libraries under certain terms and conditions. CUDA Memory Global Memory We used global memory to hold the functions values. 2) In one block I need to load into shared memory the queues of other blocks. For exponentiation using base 2 or 10, use the functions exp2() or expf2() and exp10() or expf10() rather than the functions pow() or powf(). An exception is the case where all threads in a warp address the same shared memory address, resulting in a broadcast. This spreadsheet, shown in Figure 15, is called CUDA_Occupancy_Calculator.xls and is located in the tools subdirectory of the CUDA Toolkit installation. Shared memory is a powerful feature for writing well optimized CUDA code. Therefore, it is important to be sure to compare values of like precision and to express the results within a certain tolerance rather than expecting them to be exact. For branches including just a few instructions, warp divergence generally results in marginal performance losses. 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. For devices of compute capability 2.0, the warp size is 32 threads and the number of banks is also 32. This data will thus use the L2 set-aside portion. 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. NVIDIA products are sold subject to the NVIDIA standard terms and conditions of sale supplied at the time of order acknowledgement, unless otherwise agreed in an individual sales agreement signed by authorized representatives of NVIDIA and customer (Terms of Sale). The number of blocks in a grid should be larger than the number of multiprocessors so that all multiprocessors have at least one block to execute. 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. For optimal performance, users should manually tune the NUMA characteristics of their application. Staging Ground Beta 1 Recap, and Reviewers needed for Beta 2, Atomic operations on Shared Memory in CUDA. This guide refers to and relies on several other documents that you should have at your disposal for reference, all of which are available at no cost from the CUDA website https://docs.nvidia.com/cuda/. To do this, the simpleMultiply kernel (Unoptimized matrix multiplication) calculates the output elements of a tile of matrix C. In Unoptimized matrix multiplication, a, b, and c are pointers to global memory for the matrices A, B, and C, respectively; blockDim.x, blockDim.y, and TILE_DIM are all equal to w. Each thread in the wxw-thread block calculates one element in a tile of C. row and col are the row and column of the element in C being calculated by a particular thread. 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. 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. Shared memory enables cooperation between threads in a block. High Priority: Use the effective bandwidth of your computation as a metric when measuring performance and optimization benefits. Conversion to Warp isn't possible for Week 6-7 because there is no support for shared memory or block level synchronization. A natural decomposition of the problem is to use a block and tile size of wxw threads. By comparison, the smallest executable unit of parallelism on a CUDA device comprises 32 threads (termed a warp of threads). If not, my suggestion would be to start by breaking your work into separate kernels, and using the kernel launch(es) as sync points. (To determine the latter number, see the deviceQuery CUDA Sample or refer to Compute Capabilities in the CUDA C++ Programming Guide.) CUDA work occurs within a process space for a particular GPU known as a context. In this guide, they represent a typical case. For applications that need additional functionality or performance beyond what existing parallel libraries or parallelizing compilers can provide, parallel programming languages such as CUDA C++ that integrate seamlessly with existing sequential code are essential. What if you need multiple dynamically sized arrays in a single kernel? You must declare a single extern unsized array as before, and use pointers into it to divide it into multiple arrays, as in the following excerpt. The NVML API is shipped with the CUDA Toolkit (since version 8.0) and is also available standalone on the NVIDIA developer website as part of the GPU Deployment Kit through a single header file accompanied by PDF documentation, stub libraries, and sample applications; see https://developer.nvidia.com/gpu-deployment-kit. In general, they should be avoided, because compared to peak capabilities any architecture processes these memory access patterns at a low efficiency. Details about occupancy are displayed in the Occupancy section. 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. Even though each multiprocessor contains thousands of 32-bit registers (see Features and Technical Specifications of the CUDA C++ Programming Guide), these are partitioned among concurrent threads. 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. Non-default streams (streams other than stream 0) are required for concurrent execution because 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. A system with multiple GPUs may contain GPUs of different hardware versions and capabilities. 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. 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. Theoretical bandwidth can be calculated using hardware specifications available in the product literature. Starting with CUDA 11, the toolkit versions are based on an industry-standard semantic versioning scheme: .X.Y.Z, where: .X stands for the major version - APIs have changed and binary compatibility is broken. Medium Priority: Prefer faster, more specialized math functions over slower, more general ones when possible. Device 0 of this system has compute capability 7.0. . Sometimes, the compiler may unroll loops or optimize out if or switch statements by using branch predication instead. For most purposes, the key point is that the larger the parallelizable portion P is, the greater the potential speedup. Block-column matrix (A) multiplied by block-row matrix (B) with resulting product matrix (C). The dimension and size of blocks per grid and the dimension and size of threads per block are both important factors. Concurrent copy and execute illustrates the basic technique. On all CUDA-enabled devices, it is possible to overlap host computation with asynchronous data transfers and with device computations. 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. 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. Applications that do not check for CUDA API errors could at times run to completion without having noticed that the data calculated by the GPU is incomplete, invalid, or uninitialized. These copy instructions are asynchronous, with respect to computation and allow users to explicitly control overlap of compute with data movement from global memory into the SM. Because the driver may interleave execution of CUDA calls from other non-default streams, calls in other streams may be included in the timing. It is important to use the same divisor when calculating theoretical and effective bandwidth so that the comparison is valid. This feature enables CUDA kernels to overlap copying data from global to shared memory with computation. Shared memory is magnitudes faster to access than global memory. As described in Memory Optimizations of this guide, bandwidth can be dramatically affected by the choice of memory in which data is stored, how the data is laid out and the order in which it is accessed, as well as other factors. rev2023.3.3.43278. Furthermore, the pinning of system memory is a heavyweight operation compared to most normal system memory allocations, so as with all optimizations, test the application and the systems it runs on for optimal performance parameters. 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. 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. An example is transposing [1209, 9] of any type and 32 tile size. No contractual obligations are formed either directly or indirectly by this document. However, if multiple addresses of a memory request map to the same memory bank, the accesses are serialized. Applications using the new API can load the final device code directly using driver APIs cuModuleLoadData and cuModuleLoadDataEx. The following sections discuss some caveats and considerations. Just-in-time compilation increases application load time but allows applications to benefit from latest compiler improvements. Mapped pinned host memory allows you to overlap CPU-GPU memory transfers with computation while avoiding the use of CUDA streams. Certain hardware features are not described by the compute capability. 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. We will note some of them later on in the document. 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. Like the other calls in this listing, their specific operation, parameters, and return values are described in the CUDA Toolkit Reference Manual. This chapter discusses the various kinds of memory on the host and device and how best to set up data items to use the memory effectively. When we can, we should use registers. 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.

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