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. 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. For more information on this pragma, refer to the CUDA C++ Programming Guide. 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. In certain addressing situations, reading device memory through texture fetching can be an advantageous alternative to reading device memory from global or constant memory. \times (4096/8) \times 2 \right) \div 10^{9} = 898\text{GB/s}\). It is however usually more effective to use a high-level programming language such as C++. Customer should obtain the latest relevant information before placing orders and should verify that such information is current and complete. Hardware Acceleration for Split Arrive/Wait Barrier, 1.4.1.4. When an application is built for multiple compute capabilities simultaneously (using several instances of the -gencode flag to nvcc), the binaries for the specified compute capabilities are combined into the executable, and the CUDA Driver selects the most appropriate binary at runtime according to the compute capability of the present device. That is, a thread can safely read a memory location via texture if the location has been updated by a previous kernel call or memory copy, but not if it has been previously updated by the same thread or another thread within the same kernel call. More information on cubins, PTX and application compatibility can be found in the CUDA C++ Programming Guide. // Number of bytes for persisting accesses. For this workflow, a new nvptxcompiler_static library is shipped with the CUDA Toolkit. For global memory accesses, this comparison of requested memory bandwidth to actual memory bandwidth is reported by the Global Memory Load Efficiency and Global Memory Store Efficiency metrics. 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. 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. This does not mean that application binaries compiled using an older toolkit will not be supported anymore. For more details on the new warp wide reduction operations refer to Warp Reduce Functions in the CUDA C++ Programming Guide. On Windows, if the CUDA Runtime or other dynamically-linked CUDA Toolkit library is placed in the same directory as the executable, Windows will locate it automatically. A diagram depicting the timeline of execution for the two code segments is shown in Figure 1, and nStreams is equal to 4 for Staged concurrent copy and execute in the bottom half of the figure. It can be copied into the same directory as the application executable or into a subdirectory of that installation path. Note also that whenever sine and cosine of the same argument are computed, the sincos family of instructions should be used to optimize performance: __sincosf() for single-precision fast math (see next paragraph). For Windows 8, SetDefaultDLLDirectories() and AddDllDirectory() should be used instead of SetDllDirectory(). Using Kolmogorov complexity to measure difficulty of problems? The cause of the difference is shared memory bank conflicts. Shared memory is allocated per thread block, so all threads in the block have access to the same shared memory. When using the driver APIs directly, we recommend using the new driver entry point access API (cuGetProcAddress) documented here: CUDA Driver API :: CUDA Toolkit Documentation. It is also the only way for applications to run on devices that did not exist at the time the application was compiled. Adjust kernel launch configuration to maximize device utilization. Thanks for contributing an answer to Stack Overflow! When multiple threads in a block use the same data from global memory, shared memory can be used to access the data from global memory only once. FP16 / FP32 CUDA shared memory of other blocks - Stack Overflow This is shown in Figure 1. The following table presents the evolution of matrix instruction sizes and supported data types for Tensor Cores across different GPU architecture generations. The following example is based on gprof, which is an open-source profiler for Linux platforms from the GNU Binutils collection. Static linking makes the executable slightly larger, but it ensures that the correct version of runtime library functions are included in the application binary without requiring separate redistribution of the CUDA Runtime library. Creating additional contexts incurs memory overhead for per-context data and time overhead for context switching. Follow semantic versioning for your librarys soname. In this calculation, the memory clock rate is converted in to Hz, multiplied by the interface width (divided by 8, to convert bits to bytes) and multiplied by 2 due to the double data rate. The repeated reading of the B tile can be eliminated by reading it into shared memory once (Improvement by reading additional data into shared memory). Such a pattern is shown in Figure 3. The Perl bindings are provided via CPAN and the Python bindings via PyPI. .Y stands for the minor version - Introduction of new APIs, deprecation of old APIs, and source compatibility might be broken but binary compatibility is maintained. (tens of kBs capacity) Global memory is main memory (GDDR,HBM, (1-32 GB)) and data is cached by L2,L1 caches. . Shared Memory in Matrix Multiplication (C=AAT), 9.2.3.4. A natural decomposition of the problem is to use a block and tile size of wxw threads. This capability makes them well suited to computations that can leverage parallel execution. Also of note is the Thrust library, which is a parallel C++ template library similar to the C++ Standard Template Library. By exposing parallelism to the compiler, directives allow the compiler to do the detailed work of mapping the computation onto the parallel architecture. To analyze performance, it is necessary to consider how warps access global memory in the for loop. Formulae for exponentiation by small fractions, Sample CUDA configuration data reported by deviceQuery, +-----------------------------------------------------------------------------+, |-------------------------------+----------------------+----------------------+, |===============================+======================+======================|, +-------------------------------+----------------------+----------------------+, |=============================================================================|, cudaDevAttrCanUseHostPointerForRegisteredMem, 1.3. A Sequential but Misaligned Access Pattern, 9.2.2.2. CUDA 11.0 introduces an async-copy feature that can be used within device code to explicitly manage the asynchronous copying of data from global memory to shared memory. Shared memory can be helpful in several situations, such as helping to coalesce or eliminate redundant access to global memory. Note that the NVIDIA Tesla A100 GPU has 40 MB of total L2 cache capacity. Whether a device has this capability is indicated by the concurrentKernels field of the cudaDeviceProp structure (or listed in the output of the deviceQuery CUDA Sample). Functions following the __functionName() naming convention map directly to the hardware level. 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. The maximum number of thread blocks per SM is 32 for devices of compute capability 8.0 (i.e., A100 GPUs) and 16 for GPUs with compute capability 8.6. In the NVIDIA Ampere GPU architecture remote NVLINK accesses go through a Link TLB on the remote GPU. 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. Armed with this knowledge, the developer can evaluate these bottlenecks for parallelization and start to investigate GPU acceleration. Using a profiler, the developer can identify such hotspots and start to compile a list of candidates for parallelization. The throughput of __sinf(x), __cosf(x), and__expf(x) is much greater than that of sinf(x), cosf(x), and expf(x). The various principal traits of the memory types are shown in Table 1. Cached in L1 and L2 by default on devices of compute capability 6.0 and 7.x; cached only in L2 by default on devices of lower compute capabilities, though some allow opt-in to caching in L1 as well via compilation flags. Last updated on Feb 27, 2023. cudaFuncAttributePreferredSharedMemoryCarveout, 1. Support for Bfloat16 Tensor Core, through HMMA instructions. By comparison, the smallest executable unit of parallelism on a CUDA device comprises 32 threads (termed a warp of threads). 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. For example, we can write our CUDA kernels as a collection of many short __device__ functions rather than one large monolithic __global__ function; each device function can be tested independently before hooking them all together. For example, it may be desirable to use a 64x64 element shared memory array in a kernel, but because the maximum number of threads per block is 1024, it is not possible to launch a kernel with 64x64 threads per block. 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. Code that cannot be sufficiently parallelized should run on the host, unless doing so would result in excessive transfers between the host and the device. But this technique is still useful for other access patterns, as Ill show in the next post.). With wrap, x is replaced by frac(x) where frac(x) = x - floor(x). For more details on the new Tensor Core operations refer to the Warp Matrix Multiply section in the CUDA C++ Programming Guide. Is it possible to create a concave light? 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. 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 versions of the components in the toolkit are available in this table. Reading from a texture while writing to its underlying global memory array in the same kernel launch should be avoided because the texture caches are read-only and are not invalidated when the associated global memory is modified. A threads execution can only proceed past a __syncthreads() after all threads in its block have executed the __syncthreads(). CUDA: Shared memory allocation with overlapping borders These barriers can also be used alongside the asynchronous copy. These memory spaces include global, local, shared, texture, and registers, as shown in Figure 2. Lets say that two threads A and B each load a data element from global memory and store it to shared memory. By using new CUDA versions, users can benefit from new CUDA programming model APIs, compiler optimizations and math library features. Use several smaller thread blocks rather than one large thread block per multiprocessor if latency affects performance. Testing of all parameters of each product is not necessarily performed by NVIDIA. 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. Per thread resources required by a CUDA kernel might limit the maximum block size in an unwanted way. Can this be done? Declare shared memory in CUDA C/C++ device code using the__shared__variable declaration specifier. Since there are many possible optimizations that can be considered, having a good understanding of the needs of the application can help to make the process as smooth as possible. We fix the num_bytes in the access window to 20 MB and tune the hitRatio such that a random 20 MB of the total persistent data is resident in the L2 set-aside cache portion. Other company and product names may be trademarks of the respective companies with which they are associated. Certain memory access patterns enable the hardware to coalesce groups of reads or writes of multiple data items into one operation. Because the minimum memory transaction size is larger than most word sizes, the actual memory throughput required for a kernel can include the transfer of data not used by the kernel. All kernel launches are asynchronous, as are memory-copy functions with the Async suffix on their names. Concurrent copy and execute demonstrates how to overlap kernel execution with asynchronous data transfer. For example, servers that have two 32 core processors can run only 64 threads concurrently (or small multiple of that if the CPUs support simultaneous multithreading). Therefore, choosing sensible thread block sizes, such as multiples of the warp size (i.e., 32 on current GPUs), facilitates memory accesses by warps that are properly aligned. PTX defines a virtual machine and ISA for general purpose parallel thread execution. 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. 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. The maximum number of registers per thread is 255. 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. When JIT compilation of PTX device code is used, the NVIDIA driver caches the resulting binary code on disk. The programming guide for tuning CUDA Applications for GPUs based on the NVIDIA Ampere GPU Architecture. The CUDA Runtime handles kernel loading and setting up kernel parameters and launch configuration before the kernel is launched. 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. 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. It is important to use the same divisor when calculating theoretical and effective bandwidth so that the comparison is valid. Register dependencies arise when an instruction uses a result stored in a register written by an instruction before it. 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. 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. 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. 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. Of these different memory spaces, global memory is the most plentiful; see Features and Technical Specifications of the CUDA C++ Programming Guide for the amounts of memory available in each memory space at each compute capability level. As seen above, in the case of misaligned sequential accesses, caches help to alleviate the performance impact. Details about occupancy are displayed in the Occupancy section. Sample CUDA configuration data reported by deviceQuery. CUDA Shared Memory - Oak Ridge Leadership Computing Facility Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, How Intuit democratizes AI development across teams through reusability. Increment major versions when there are ABI breaking changes such as API deprecation and modifications. These transfers are costly in terms of performance and should be minimized. 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. Some recent Linux distributions enable automatic NUMA balancing (or AutoNUMA) by default. A device in which work is poorly balanced across the multiprocessors will deliver suboptimal performance. 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. Although each of these instructions is scheduled for execution, only the instructions with a true predicate are actually executed. What is CUDA memory? - Quora //Such that up to 20MB of data is resident. This chapter examines issues that can affect the correctness of returned data and points to appropriate solutions. Dealing with relocatable objects is not yet supported, therefore the cuLink* set of APIs in the CUDA driver will not work with enhanced compatibility. These instructions also avoid using extra registers for memory copies and can also bypass the L1 cache. Randomly accessing. Users wishing to take advantage of such a feature should query its availability with a dynamic check in the code: Alternatively the applications interface might not work at all without a new CUDA driver and then its best to return an error right away: A new error code is added to indicate that the functionality is missing from the driver you are running against: cudaErrorCallRequiresNewerDriver. So there is no chance of memory corruption caused by overcommitting shared memory. Instead, strategies can be applied incrementally as they are learned. 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. The compiler must on occasion insert conversion instructions, introducing additional execution cycles. If L1-caching is enabled on these devices, the number of required transactions is equal to the number of required 128-byte aligned segments. CUDA C++ provides a simple path for users familiar with the C++ programming language to easily write programs for execution by the device. For those exponentiations where the exponent is not exactly representable as a floating-point number, such as 1/3, this can also provide much more accurate results, as use of pow() magnifies the initial representational error. 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. However, it is best to avoid accessing global memory whenever possible. The remainder of the kernel code is identical to the staticReverse() kernel. Both the CUDA driver and the CUDA runtime are not source compatible across the different SDK releases. In reality, most applications do not exhibit perfectly linear strong scaling, even if they do exhibit some degree of strong scaling. These results should be compared with those in Table 2. First, we set aside 30 MB of the L2 cache for persisting accesses using cudaDeviceSetLimit(), as discussed above. CUDA driver - User-mode driver component used to run CUDA applications (e.g. cudaDeviceSynchronize()blocks the calling CPU thread until all CUDA calls previously issued by the thread are completed. As such, the constant cache is best when threads in the same warp accesses only a few distinct locations. 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). Pinned memory should not be overused. 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. Devices of compute capability 3.x have configurable bank size, which can be set using cudaDeviceSetSharedMemConfig() to either four bytes (cudaSharedMemBankSizeFourByte, thedefault) or eight bytes (cudaSharedMemBankSizeEightByte). These are the same contexts used implicitly by the CUDA Runtime when there is not already a current context for a thread. The kernel also uses the default stream, and it will not begin execution until the memory copy completes; therefore, no explicit synchronization is needed. On devices that are capable of concurrent kernel execution, streams can also be used to execute multiple kernels simultaneously to more fully take advantage of the devices multiprocessors. TF32 provides 8-bit exponent, 10-bit mantissa and 1 sign-bit. This recommendation is subject to resource availability; therefore, it should be determined in the context of the second execution parameter - the number of threads per block, or block size - as well as shared memory usage. Computing a row of a tile in C using one row of A and an entire tile of B. The -use_fast_math compiler option of nvcc coerces every functionName() call to the equivalent __functionName() call. To check for errors occurring during kernel launches using the <<<>>> syntax, which does not return any error code, the return code of cudaGetLastError() should be checked immediately after the kernel launch. If from any of the four 32-byte segments only a subset of the words are requested (e.g. Any PTX device code loaded by an application at runtime is compiled further to binary code by the device driver. CUDA: Explainer of a kernel with 2D blocks, shared memory, atomics How do I align things in the following tabular environment? 1 Answer Sorted by: 2 You don't need to worry about this. ? Furthermore, register allocations are rounded up to the nearest 256 registers per warp. (Consider what would happen to the memory addresses accessed by the second, third, and subsequent thread blocks if the thread block size was not a multiple of warp size, for example.). Overall, developers can expect similar occupancy as on Volta without changes to their application. 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. This action leads to a load of eight L2 cache segments per warp on the Tesla V100 (compute capability 7.0). Medium Priority: Use shared memory to avoid redundant transfers from global memory. This approach permits some overlapping of the data transfer and execution. CUDA applications are built against the CUDA Runtime library, which handles device, memory, and kernel management. The cudaEventElapsedTime() function returns the time elapsed between the recording of the start and stop events. It can be simpler to view N as a very large number, which essentially transforms the equation into \(S = 1/(1 - P)\). Improvement by reading additional data into shared memory. 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. 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, 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. 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. A system with multiple GPUs may contain GPUs of different hardware versions and capabilities. Fetching ECC bits for each memory transaction also reduced the effective bandwidth by approximately 20% compared to the same GPU with ECC disabled, though the exact impact of ECC on bandwidth can be higher and depends on the memory access pattern. 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. Applications using the new API can load the final device code directly using driver APIs cuModuleLoadData and cuModuleLoadDataEx. One of the key differences is the fused multiply-add (FMA) instruction, which combines multiply-add operations into a single instruction execution. 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. 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?. Each version of the CUDA Toolkit (and runtime) requires a minimum version of the NVIDIA driver. The types of operations are an additional factor, as additions have different complexity profiles than, for example, trigonometric functions. If multiple CUDA application processes access the same GPU concurrently, this almost always implies multiple contexts, since a context is tied to a particular host process unless Multi-Process Service is in use. x86 processors can use an 80-bit double extended precision math when performing floating-point calculations. 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. Consequently, its important to understand the characteristics of the architecture. For example, improving occupancy from 66 percent to 100 percent generally does not translate to a similar increase in performance. For example, many kernels have complex addressing logic for accessing memory in addition to their actual computation. Shared memory is specified by the device architecture and is measured on per-block basis. This kernel has an effective bandwidth of 144.4 GB/s on an NVIDIA Tesla V100. If this set-aside portion is not used by persistent accesses, then streaming or normal data accesses can use it. On parallel systems, it is possible to run into difficulties not typically found in traditional serial-oriented programming. Because transfers should be minimized, programs that run multiple kernels on the same data should favor leaving the data on the device between kernel calls, rather than transferring intermediate results to the host and then sending them back to the device for subsequent calculations. Other company and product names may be trademarks of the respective companies with which they are associated. Shared memory is extremely fast, user managed, on-chip memory that can be used to share data between threads within a thread block. See the nvidia-smi documenation for details. For example, the asyncEngineCount field of the device property structure indicates whether overlapping kernel execution and data transfers is possible (and, if so, how many concurrent transfers are possible); likewise, the canMapHostMemory field indicates whether zero-copy data transfers can be performed. This document is not a commitment to develop, release, or deliver any Material (defined below), code, or functionality.
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