Dont expose ABI structures that can change. Strong Scaling and Amdahls Law describes strong scaling, which allows us to set an upper bound for the speedup with a fixed problem size. Can this be done? CUDA Memory Rules Currently can only transfer data from host to global (and constant memory) and not host directly to shared. In particular, developers should note the number of multiprocessors on the device, the number of registers and the amount of memory available, and any special capabilities 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. 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. 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. --ptxas-options=-v or -Xptxas=-v lists per-kernel register, shared, and constant memory usage. 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. On the other hand, if the data is only accessed once, such data accesses can be considered to be streaming. This code reverses the data in a 64-element array using shared memory. 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. Examples include modeling fluids or structures as meshes or grids and some Monte Carlo simulations, where increasing the problem size provides increased accuracy. 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 current GPU core temperature is reported, along with fan speeds for products with active cooling. .Z stands for the release/patch version - new updates and patches will increment this. This is done by carefully choosing the execution configuration of each kernel launch. For recent versions of CUDA hardware, misaligned data accesses are not a big issue. 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). 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. To keep the kernels simple, M and N are multiples of 32, since the warp size (w) is 32 for current devices. 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. To view a librarys install name, use the otool -L command: The binary compatibility version of the CUDA libraries on Windows is indicated as part of the filename. It then explores how bandwidth affects performance metrics and how to mitigate some of the challenges it poses. Each floating-point arithmetic operation involves a certain amount of rounding. The interface is augmented to retrieve either the PTX or cubin if an actual architecture is specified. 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. For best performance, there should be some coherence in memory access by adjacent threads running on the device. Finally, this product is divided by 109 to convert the result to GB/s. 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. 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. Applications compiled with CUDA toolkit versions as old as 3.2 will run on newer drivers. Fast, low-precision interpolation between texels, Valid only if the texture reference returns floating-point data, Can be used only with normalized texture coordinates, 1 The automatic handling of boundary cases in the bottom row of Table 4 refers to how a texture coordinate is resolved when it falls outside the valid addressing range. CUDA shared memory of other blocks - Stack Overflow FP16 / FP32
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. 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. Because execution within a stream occurs sequentially, none of the kernels will launch until the data transfers in their respective streams complete. NVIDIA hereby expressly objects to applying any customer general terms and conditions with regards to the purchase of the NVIDIA product referenced in this document. To maintain architectural compatibility, static shared memory allocations remain limited to 48 KB, and an explicit opt-in is also required to enable dynamic allocations above this limit. While the contents can be used as a reference manual, you should be aware that some topics are revisited in different contexts as various programming and configuration topics are explored. 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. However, a few rules of thumb should be followed: Threads per block should be a multiple of warp size to avoid wasting computation on under-populated warps and to facilitate coalescing. In such a case, the bandwidth would be 836.4 GiB/s. What sort of strategies would a medieval military use against a fantasy giant? Avoid long sequences of diverged execution by threads within the same warp. Delays in rolling out new NVIDIA drivers could mean that users of such systems may not have access to new features available in CUDA releases. The third generation NVLink has the same bi-directional data rate of 50 GB/s per link, but uses half the number of signal pairs to achieve this bandwidth. Unlike the CUDA Driver, the CUDA Runtime guarantees neither forward nor backward binary compatibility across versions. However, if multiple addresses of a memory request map to the same memory bank, the accesses are serialized. The following example is based on gprof, which is an open-source profiler for Linux platforms from the GNU Binutils collection. Having identified the hotspots and having done the basic exercises to set goals and expectations, the developer needs to parallelize the code. For portability, that is, to be able to execute code on future GPU architectures with higher compute capability (for which no binary code can be generated yet), an application must load PTX code that will be just-in-time compiled by the NVIDIA driver for these future devices. This variant simply uses the transpose of A in place of B, so C = AAT. Finally, particular attention must be paid to control flow instructions due to the SIMT (single instruction multiple thread) nature of the device. With wrap, x is replaced by frac(x) where frac(x) = x - floor(x). Details about occupancy are displayed in the Occupancy section. Figure 4 corresponds to this misalignments) The effective bandwidth for the copy with various offsets on an NVIDIA Tesla V100 (compute capability 7.0) is shown in Figure 5. Even a relatively slow kernel may be advantageous if it avoids one or more transfers between host and device memory. Before tackling other hotspots to improve the total speedup, the developer should consider taking the partially parallelized implementation and carry it through to production. See the nvidia-smi documenation for details. High Priority: Avoid different execution paths within the same warp. The remainder of the kernel code is identical to the staticReverse() kernel. Devices of compute capability 1.3 and higher provide native support for double-precision floating-point values (that is, values 64 bits wide). Computing a row of a tile. Because the data is not cached on the GPU, mapped pinned memory should be read or written only once, and the global loads and stores that read and write the memory should be coalesced. Using shared memory to coalesce global reads. A portion of the L2 cache can be set aside for persistent accesses to a data region in global memory. If instead i is declared as signed, where the overflow semantics are undefined, the compiler has more leeway to use these optimizations. 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. To use dynamic linking with the CUDA Runtime when using the nvcc from CUDA 5.5 or later to link the application, add the --cudart=shared flag to the link command line; otherwise the statically-linked CUDA Runtime library is used by default. Memory instructions include any instruction that reads from or writes to shared, local, or global memory. By default, the nvcc compiler generates IEEE-compliant code, but it also provides options to generate code that somewhat less accurate but faster: -ftz=true (denormalized numbers are flushed to zero), -prec-sqrt=false (less precise square root). This is called just-in-time compilation (JIT). A device in which work is poorly balanced across the multiprocessors will deliver suboptimal performance. This limitation is not specific to CUDA, but an inherent part of parallel computation on floating-point values. For example, cuMemMap APIs or any of APIs introduced prior to CUDA 11.0, such as cudaDeviceSynchronize, do not require a driver upgrade. We define binary compatibility as a set of guarantees provided by the library, where an application targeting the said library will continue to work when dynamically linked against a different version of the library. Devices to be made visible to the application should be included as a comma-separated list in terms of the system-wide list of enumerable devices. The L2 cache set-aside size for persisting accesses may be adjusted, within limits: Mapping of user data to L2 set-aside portion can be controlled using an access policy window on a CUDA stream or CUDA graph kernel node. Detecting Hardware and Software Configuration. On all CUDA-enabled devices, it is possible to overlap host computation with asynchronous data transfers and with device computations. For example, transferring two matrices to the device to perform a matrix addition and then transferring the results back to the host will not realize much performance benefit. All CUDA threads can access it for read and write. 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. Using these data items, the peak theoretical memory bandwidth of the NVIDIA Tesla V100 is 898 GB/s: \(\left. The CUDA Toolkit Samples provide several helper functions for error checking with the various CUDA APIs; these helper functions are located in the samples/common/inc/helper_cuda.h file in the CUDA Toolkit. Overall Performance Optimization Strategies, https://developer.nvidia.com/nsight-visual-studio-edition, https://developer.nvidia.com/debugging-solutions, https://developer.nvidia.com/content/precision-performance-floating-point-and-ieee-754-compliance-nvidia-gpus, Asynchronous and Overlapping Transfers with Computation, CUDA Driver API :: CUDA Toolkit Documentation, dynamically-linked version of the CUDA Runtime library, Where to Install Redistributed CUDA Libraries, https://developer.nvidia.com/gpu-deployment-kit, https://developer.nvidia.com/nvidia-management-library-nvml, https://developer.nvidia.com/cluster-management. CUDA reserves 1 KB of shared memory per thread block. Copyright 2007-2023, NVIDIA Corporation & Affiliates. The results are shown in the chart below, where we see good performance regardless of whether the persistent data fits in the L2 set-aside or not. cudaOccupancyMaxActiveBlocksPerMultiprocessor, to dynamically select launch configurations based on runtime parameters. Be aware that CPU-to-GPU synchronization points such as those mentioned in this section imply a stall in the GPUs processing pipeline and should thus be used sparingly to minimize their performance impact. By reversing the array using shared memory we are able to have all global memory reads and writes performed with unit stride, achieving full coalescing on any CUDA GPU. As described in Asynchronous and Overlapping Transfers with Computation, CUDA streams can be used to overlap kernel execution with data transfers. 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. PTX programs are translated at load time to the target hardware instruction set via the JIT Compiler which is part of the CUDA driver. For some fractional exponents, exponentiation can be accelerated significantly compared to the use of pow() by using square roots, cube roots, and their inverses. No. The larger N is(that is, the greater the number of processors), the smaller the P/N fraction. In many applications, a combination of strong and weak scaling is desirable. NVIDIA-SMI can be used to configure a GPU for exclusive process mode, which limits the number of contexts per GPU to one. For exponentiation with an exponent of 1/3, use the cbrt() or cbrtf() function rather than the generic exponentiation functions pow() or powf(), as the former are significantly faster than the latter. In the next post I will continue our discussion of shared memory by using it to optimize a matrix transpose. Local memory is used only to hold automatic variables. Connect and share knowledge within a single location that is structured and easy to search. For example, many kernels have complex addressing logic for accessing memory in addition to their actual computation. The constant memory space is cached. Within each iteration of the for loop, a value in shared memory is broadcast to all threads in a warp. When our CUDA 11.1 application (i.e. Flow control instructions (if, switch, do, for, while) can significantly affect the instruction throughput by causing threads of the same warp to diverge; that is, to follow different execution paths. The CUDA runtime has relaxed the minimum driver version check and thus no longer requires a driver upgrade when moving to a new minor release. 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. In reality, most applications do not exhibit perfectly linear strong scaling, even if they do exhibit some degree of strong scaling. For certain devices of compute capability 5.2, L1-caching of accesses to global memory can be optionally enabled. The host system and the device each have their own distinct attached physical memories 1. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Adjacent threads accessing memory with a stride of 2. Likewise, for exponentation with an exponent of -1/3, use rcbrt() or rcbrtf(). This makes the code run faster at the cost of diminished precision and accuracy. One of several factors that determine occupancy is register availability. Data should be kept on the device as long as possible. This chapter contains a summary of the recommendations for optimization that are explained in this document. 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. The difference is in how threads in a half warp access elements of A in the second term, a[col*TILE_DIM+i], for each iteration i. The dynamic shared memory kernel, dynamicReverse(), declares the shared memory array using an unsized extern array syntax, extern shared int s[] (note the empty brackets and use of the extern specifier). If from any of the four 32-byte segments only a subset of the words are requested (e.g. For optimal performance, users should manually tune the NUMA characteristics of their application. The CUDA Runtime API provides developers with high-level C++ interface for simplified management of devices, kernel executions etc., While the CUDA driver API provides (CUDA Driver API) a low-level programming interface for applications to target NVIDIA hardware. Support for bitwise AND along with bitwise XOR which was introduced in Turing, through BMMA instructions. For further details on the programming features discussed in this guide, please refer to the CUDA C++ Programming Guide. These accept one of three options:cudaFuncCachePreferNone, cudaFuncCachePreferShared, and cudaFuncCachePreferL1. We can then launch this kernel onto the GPU and retrieve the results without requiring major rewrites to the rest of our application. Warp level support for Reduction Operations, 1.4.2.1. While multiple contexts (and their associated resources such as global memory allocations) can be allocated concurrently on a given GPU, only one of these contexts can execute work at any given moment on that GPU; contexts sharing the same GPU are time-sliced. 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. 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). One of the main reasons a new toolchain requires a new minimum driver is to handle the JIT compilation of PTX code and the JIT linking of binary code. The number of registers available, the maximum number of simultaneous threads resident on each multiprocessor, and the register allocation granularity vary over different compute capabilities. The optimal NUMA tuning will depend on the characteristics and desired hardware affinities of each application and node, but in general applications computing on NVIDIA GPUs are advised to choose a policy that disables automatic NUMA balancing. This microbenchmark uses a 1024 MB region in GPU global memory. Thrust provides a rich collection of data parallel primitives such as scan, sort, and reduce, which can be composed together to implement complex algorithms with concise, readable source code. As compared to the lower-level CUDA Driver API, the CUDA Runtime greatly eases device management by providing implicit initialization, context management, and device code module management. 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. 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. Upgrading dependencies is error-prone and time consuming, and in some corner cases, can even change the semantics of a program. In this case the shared memory allocation size per thread block must be specified (in bytes) using an optional third execution configuration parameter, as in the following excerpt. Block-column matrix (A) multiplied by block-row matrix (B) with resulting product matrix (C). sorting the queues) and then a single threadblock would perform the clean-up tasks such as collecting the queues and processing in a single threadblock. This information is obtained by calling cudaGetDeviceProperties() and accessing the information in the structure it returns. Each version of the CUDA Toolkit (and runtime) requires a minimum version of the NVIDIA driver. However, this requires writing to shared memory in columns, and because of the use of wxw tiles in shared memory, this results in a stride between threads of w banks - every thread of the warp hits the same bank (Recall that w is selected as 32). A lower occupancy kernel will have more registers available per thread than a higher occupancy kernel, which may result in less register spilling to local memory; in particular, with a high degree of exposed instruction-level parallelism (ILP) it is, in some cases, possible to fully cover latency with a low occupancy. 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. The NVIDIA nvcc compiler driver converts .cu files into C++ for the host system and CUDA assembly or binary instructions for the device. We want to ensure that each change we make is correct and that it improves performance (and by how much). A copy kernel that illustrates misaligned accesses. "After the incident", I started to be more careful not to trip over things. 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. Note that cudaSetDeviceFlags() must be called prior to setting a device or making a CUDA call that requires state (that is, essentially, before a context is created). 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. Coalescing concepts are illustrated in the following simple examples. Week5 + Week8 by AkeelMedina22 Pull Request #9 mmmovania/CUDA 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. When we can, we should use registers. The compiler optimizes 1.0f/sqrtf(x) into rsqrtf() only when this does not violate IEEE-754 semantics. Also of note is the Thrust library, which is a parallel C++ template library similar to the C++ Standard Template Library. In a typical system, thousands of threads are queued up for work (in warps of 32 threads each). Shared Memory and Synchronization - GPU Programming Therefore, the total number of links available is increased to twelve in A100, versus six in V100, yielding 600 GB/s bidirectional bandwidth versus 300 GB/s for V100. Choosing the execution configuration parameters should be done in tandem; however, there are certain heuristics that apply to each parameter individually. The nature of simulating nature: A Q&A with IBM Quantum researcher Dr. Jamie We've added a "Necessary cookies only" option to the cookie consent popup. Actions that present substantial improvements for most CUDA applications have the highest priority, while small optimizations that affect only very specific situations are given a lower priority. Best practices suggest that this optimization be performed after all higher-level optimizations have been completed. By default the 48KBshared memory setting is used. An example would be modeling how two molecules interact with each other, where the molecule sizes are fixed. Replace sin(*) with sinpi(), cos(*) with cospi(), and sincos(*) with sincospi(). Memory Access Higher compute capability versions are supersets of lower (that is, earlier) versions, so they are backward compatible. (Note that the CUDA compiler considers any device code that does not contribute to a write to global memory as dead code subject to elimination, so we must at least write something out to global memory as a result of our addressing logic in order to successfully apply this strategy.). There are two options: clamp and wrap. 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. The third generation of NVIDIAs high-speed NVLink interconnect is implemented in A100 GPUs, which significantly enhances multi-GPU scalability, performance, and reliability with more links per GPU, much faster communication bandwidth, and improved error-detection and recovery features. Therefore, to accurately measure the elapsed time for a particular call or sequence of CUDA calls, it is necessary to synchronize the CPU thread with the GPU by calling cudaDeviceSynchronize() immediately before starting and stopping the CPU timer. This typically involves arithmetic on large data sets (such as matrices) where the same operation can be performed across thousands, if not millions, of elements at the same time. Shared memory banks are organized such that successive 32-bit words are assigned to successive banks and the bandwidth is 32 bits per bank per clock cycle. The compiler must on occasion insert conversion instructions, introducing additional execution cycles. 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.