If this happens, the different execution paths must be executed separately; this increases the total number of instructions executed for this warp. This means that in one of these devices, for a multiprocessor to have 100% occupancy, each thread can use at most 32 registers. The major and minor revision numbers of the compute capability are shown on the seventh line of Figure 16. Other peculiarities of floating-point arithmetic are presented in Features and Technical Specifications of the CUDA C++ Programming Guide as well as in a whitepaper and accompanying webinar on floating-point precision and performance available from https://developer.nvidia.com/content/precision-performance-floating-point-and-ieee-754-compliance-nvidia-gpus. The CUDA Runtime handles kernel loading and setting up kernel parameters and launch configuration before the kernel is launched. However, this approach of determining how register count affects occupancy does not take into account the register allocation granularity. These are the primary hardware differences between CPU hosts and GPU devices with respect to parallel programming. This metric is occupancy. Mutually exclusive execution using std::atomic? exchange data) between threadblocks, the only method is to use global memory. To keep the kernels simple, M and N are multiples of 32, since the warp size (w) is 32 for current devices. Aside from memory bank conflicts, there is no penalty for non-sequential or unaligned accesses by a warp in 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. 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. Cornell Virtual Workshop: Memory Architecture By default the 48KBshared memory setting is used. It is limited. The hardware splits a conflicting memory request into as many separate conflict-free requests as necessary, decreasing the effective bandwidth by a factor equal to the number of colliding memory requests. It is customers sole responsibility to evaluate and determine the applicability of any information contained in this document, ensure the product is suitable and fit for the application planned by customer, and perform the necessary testing for the application in order to avoid a default of the application or the product. See Registers for details. This is done by the nvcc compiler when it determines that there is insufficient register space to hold the variable. For more information on this pragma, refer to the CUDA C++ Programming Guide. CUDA Shared Memory - Oak Ridge Leadership Computing Facility Data Transfer Between Host and Device, 9.1.2. (e.g. In reality, most applications do not exhibit perfectly linear strong scaling, even if they do exhibit some degree of strong scaling. 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. PDF CUDA Memory Model The amount of performance benefit an application will realize by running on CUDA depends entirely on the extent to which it can be parallelized. However, it also can act as a constraint on occupancy. This optimization is especially important for global memory accesses, because latency of access costs hundreds of clock cycles. For an existing project, the first step is to assess the application to locate the parts of the code that are responsible for the bulk of the execution time. This Best Practices Guide is a manual to help developers obtain the best performance from NVIDIA CUDA GPUs. Low Priority: Avoid automatic conversion of doubles to floats. This is not a problem when PTX is used for future device compatibility (the most common case), but can lead to issues when used for runtime compilation. Detecting Hardware and Software Configuration. 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. On devices with GDDR memory, accessing memory in a coalesced way is even more important when ECC is turned on. The NVIDIA Ampere GPU architecture includes new Third Generation Tensor Cores that are more powerful than the Tensor Cores used in Volta and Turing SMs. Support for TF32 Tensor Core, through HMMA instructions. However, compared to cache based architectures, like CPUs, latency hiding architectures, like GPUs, tend to cope better with completely random memory access patterns. These results are substantially lower than the corresponding measurements for the C = AB kernel. Sample CUDA configuration data reported by deviceQuery. Like all CUDA Runtime API functions, this function will fail gracefully and return cudaErrorNoDevice to the application if there is no CUDA-capable GPU or cudaErrorInsufficientDriver if there is not an appropriate version of the NVIDIA Driver installed. outside your established ABI contract. In this case, multiple broadcasts from different banks are coalesced into a single multicast from the requested shared memory locations to the threads. Lets say that two threads A and B each load a data element from global memory and store it to shared memory. 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. THIS DOCUMENT AND ALL NVIDIA DESIGN SPECIFICATIONS, REFERENCE BOARDS, FILES, DRAWINGS, DIAGNOSTICS, LISTS, AND OTHER DOCUMENTS (TOGETHER AND SEPARATELY, MATERIALS) ARE BEING PROVIDED AS IS. NVIDIA MAKES NO WARRANTIES, EXPRESSED, IMPLIED, STATUTORY, OR OTHERWISE WITH RESPECT TO THE MATERIALS, AND EXPRESSLY DISCLAIMS ALL IMPLIED WARRANTIES OF NONINFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE. Low Medium Priority: Use signed integers rather than unsigned integers as loop counters. However, the SONAME of this library is given as libcublas.so.5.5: Because of this, even if -lcublas (with no version number specified) is used when linking the application, the SONAME found at link time implies that libcublas.so.5.5 is the name of the file that the dynamic loader will look for when loading the application and therefore must be the name of the file (or a symlink to the same) that is redistributed with the application. The maximum number of registers per thread can be set manually at compilation time per-file using the -maxrregcount option or per-kernel using the __launch_bounds__ qualifier (see Register Pressure). Note that the NVIDIA Tesla A100 GPU has 40 MB of total L2 cache capacity. For example, on IBM Newell POWER9 nodes (where the CPUs correspond to NUMA nodes 0 and 8), use: One of the keys to good performance is to keep the multiprocessors on the device as busy as possible. PTX programs are translated at load time to the target hardware instruction set via the JIT Compiler which is part of the CUDA driver. Using these data items, the peak theoretical memory bandwidth of the NVIDIA Tesla V100 is 898 GB/s: \(\left. Note that in Improvement by reading additional data into shared memory, a __syncthreads() call is required after reading the B tile because a warp reads data from shared memory that were written to shared memory by different warps. Not all threads need to participate. So, in the previous example, had the two matrices to be added already been on the device as a result of some previous calculation, or if the results of the addition would be used in some subsequent calculation, the matrix addition should be performed locally on the device. A place where magic is studied and practiced? 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. Although the CUDA Runtime provides the option of static linking, some libraries included in the CUDA Toolkit are available only in dynamically-linked form. 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. 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 some fractional exponents, exponentiation can be accelerated significantly compared to the use of pow() by using square roots, cube roots, and their inverses. . As with the dynamically-linked version of the CUDA Runtime library, these libraries should be bundled with the application executable when distributing that application. 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 reads of elements in transposedTile within the for loop are free of conflicts, because threads of each half warp read across rows of the tile, resulting in unit stride across the banks. Each generation of CUDA-capable device has an associated compute capability version that indicates the feature set supported by the device (see CUDA Compute Capability). 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. Weak Scaling and Gustafsons Law describes weak scaling, where the speedup is attained by growing the problem size. Both the CUDA driver and the CUDA runtime are not source compatible across the different SDK releases. 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. These exceptions, which are detailed in Features and Technical Specifications of the CUDA C++ Programming Guide, can lead to results that differ from IEEE 754 values computed on the host system. Answer (1 of 2): Shared memory has many more channels(and bandwidth) and works with much less latency. As the host and device memories are separated, items in the host memory must occasionally be communicated between device memory and host memory as described in What Runs on a CUDA-Enabled Device?. By comparison, threads on GPUs are extremely lightweight. Overall, best performance is achieved when using asynchronous copies with an element of size 8 or 16 bytes. This is advantageous with regard to both accuracy and performance. 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. Since shared memory is shared amongst threads in a thread block, it provides a mechanism for threads to cooperate. (See Data Transfer Between Host and Device.) Not requiring driver updates for new CUDA releases can mean that new versions of the software can be made available faster to users. 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. This should be our first candidate function for parallelization. The easiest option is to statically link against the CUDA Runtime. From the performance chart, the following observations can be made for this experiment. C++-style convenience wrappers (cuda_runtime.h) built on top of the C-style functions. If all threads of a warp access the same location, then constant memory can be as fast as a register access. It is customers sole responsibility to evaluate and determine the applicability of any information contained in this document, ensure the product is suitable and fit for the application planned by customer, and perform the necessary testing for the application in order to avoid a default of the application or the product. The cudaDeviceEnablePeerAccess() API call remains necessary to enable direct transfers (over either PCIe or NVLink) between GPUs. NVIDIA makes no representation or warranty that products based on this document will be suitable for any specified use. 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. If individual CUDA threads are copying elements of 16 bytes, the L1 cache can be bypassed. Copyright 2020-2023, NVIDIA Corporation & Affiliates. Data that cannot be laid out so as to enable coalescing, or that doesnt have enough locality to use the L1 or texture caches effectively, will tend to see lesser speedups when used in computations on GPUs. If you want to communicate (i.e. 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. CUDA work occurs within a process space for a particular GPU known as a context. I have locally sorted queues in different blocks of cuda. Multiple kernels executing at the same time is known as concurrent kernel execution. On the other hand, some applications designs will require some amount of refactoring to expose their inherent parallelism. Copy the results from device memory to host memory, also called device-to-host transfer. This feature enables CUDA kernels to overlap copying data from global to shared memory with computation. This is the default if using nvcc to link in CUDA 5.5 and later. Fixed value 1.0, The performance of the sliding-window benchmark with fixed hit-ratio of 1.0. FP16 / FP32 Exponentiation With Small Fractional Arguments, 14. If this set-aside portion is not used by persistent accesses, then streaming or normal data accesses can use it. Local memory is used only to hold automatic variables. CUDA devices use several memory spaces, which have different characteristics that reflect their distinct usages in CUDA applications. Kernels can be written using the CUDA instruction set architecture, called PTX, which is described in the PTX reference manual. However, as with APOD as a whole, program optimization is an iterative process (identify an opportunity for optimization, apply and test the optimization, verify the speedup achieved, and repeat), meaning that it is not necessary for a programmer to spend large amounts of time memorizing the bulk of all possible optimization strategies prior to seeing good speedups. Medium Priority: Use shared memory to avoid redundant transfers from global memory. The NVIDIA Ampere GPU architecture adds native support for warp wide reduction operations for 32-bit signed and unsigned integer operands. Before addressing specific performance tuning issues covered in this guide, refer to the NVIDIA Ampere GPU Architecture Compatibility Guide for CUDA Applications to ensure that your application is compiled in a way that is compatible with the NVIDIA Ampere GPU Architecture.
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