Since you don't indicate where your "locally sorted" data resides, this could indicate a copying of that much data at least (for example, if they are locally sorted and reside in shared memory). This situation is not different from what is available today where developers use macros to compile out features based on CUDA versions. 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 enables cooperation between threads in a block. This is possible because the distribution of the warps across the block is deterministic as mentioned in SIMT Architecture of the CUDA C++ Programming Guide. 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. If you preorder a special airline meal (e.g. A minimum of 64 threads per block should be used, and only if there are multiple concurrent blocks per multiprocessor. Lets say that two threads A and B each load a data element from global memory and store it to shared memory. Incorrect or unexpected results arise principally from issues of floating-point accuracy due to the way floating-point values are computed and stored. If we validate our addressing logic separately prior to introducing the bulk of the computation, then this will simplify any later debugging efforts. So, in clamp mode where N = 1, an x of 1.3 is clamped to 1.0; whereas in wrap mode, it is converted to 0.3. A place where magic is studied and practiced? As mentioned in Occupancy, higher occupancy does not always equate to better performance. It also disables single-precision denormal support and lowers the precision of single-precision division in general. It presents established parallelization and optimization techniques and explains coding metaphors and idioms that can greatly simplify programming for CUDA-capable GPU architectures. Devices of compute capability 2.0 and later support a special addressing mode called Unified Virtual Addressing (UVA) on 64-bit Linux and Windows. This helps in reducing cache thrashing. Register storage enables threads to keep local variables nearby for low-latency access. These many-way bank conflicts are very expensive. Block-column matrix multiplied by block-row matrix. Instead, strategies can be applied incrementally as they are learned. 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. .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. In this section, we will review the usage patterns that may require new user workflows when taking advantage of the compatibility features of the CUDA platform. On parallel systems, it is possible to run into difficulties not typically found in traditional serial-oriented programming. The cudaChooseDevice() function can be used to select the device that most closely matches a desired set of features. The CUDA Driver API has a versioned C-style ABI, which guarantees that applications that were running against an older driver (for example CUDA 3.2) will still run and function correctly against a modern driver (for example one shipped with CUDA 11.0). Register pressure occurs when there are not enough registers available for a given task. A stride of 2 results in a 50% of load/store efficiency since half the elements in the transaction are not used and represent wasted bandwidth. 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. Creating additional contexts incurs memory overhead for per-context data and time overhead for context switching. Consequently, its important to understand the characteristics of the architecture. More details are available in the CUDA C++ Programming Guide. In such cases, and when the execution time (tE) exceeds the transfer time (tT), a rough estimate for the overall time is tE + tT/nStreams for the staged version versus tE + tT for the sequential version. The size is implicitly determined from the third execution configuration parameter when the kernel is launched. I think this pretty much implies that you are going to have the place the heads of each queue in global memory. However, the set of registers (known as the register file) is a limited commodity that all threads resident on a multiprocessor must share. NVIDIA accepts no liability for inclusion and/or use of NVIDIA products in such equipment or applications and therefore such inclusion and/or use is at customers own risk. The amount of performance benefit an application will realize by running on CUDA depends entirely on the extent to which it can be parallelized. Therefore, to get the largest speedup for a fixed problem size, it is worthwhile to spend effort on increasing P, maximizing the amount of code that can be parallelized. This is shown in Figure 1. 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. outside your established ABI contract. Whether a device has this capability is indicated by the asyncEngineCount field of the cudaDeviceProp structure (or listed in the output of the deviceQuery CUDA Sample). Within each iteration of the for loop, a value in shared memory is broadcast to all threads in a warp. A natural decomposition of the problem is to use a block and tile size of wxw threads. This document is provided for information purposes only and shall not be regarded as a warranty of a certain functionality, condition, or quality of a product. This action leads to a load of eight L2 cache segments per warp on the Tesla V100 (compute capability 7.0). Hardware utilization can also be improved in some cases by designing your application so that multiple, independent kernels can execute at the same time. The kernel is executed within a loop in host code that varies the parameter offset from 0 to 32. Current utilization rates are reported for both the compute resources of the GPU and the memory interface. Thus, we can avoid the race condition described above by calling __syncthreads() after the store to shared memory and before any threads load from shared memory. In particular, there is no register-related reason to pack data into vector data types such as float4 or int4 types. Floating Point Math Is not Associative, 8.2.3. This guide introduces the Assess, Parallelize, Optimize, Deploy(APOD) design cycle for applications with the goal of helping application developers to rapidly identify the portions of their code that would most readily benefit from GPU acceleration, rapidly realize that benefit, and begin leveraging the resulting speedups in production as early as possible. Its important to be aware that calling __syncthreads() in divergent code is undefined and can lead to deadlockall threads within a thread block must call __syncthreads() at the same point. While processors are evolving to expose more fine-grained parallelism to the programmer, many existing applications have evolved either as serial codes or as coarse-grained parallel codes (for example, where the data is decomposed into regions processed in parallel, with sub-regions shared using MPI). 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. Hence, access to local memory is as expensive as access to global memory. Weak Scaling and Gustafsons Law, 3.1.3.3. 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). Before implementing lower priority recommendations, it is good practice to make sure all higher priority recommendations that are relevant have already been applied. 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. 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. It can be simpler to view N as a very large number, which essentially transforms the equation into \(S = 1/(1 - P)\). Host memory allocations pinned after-the-fact via cudaHostRegister(), however, will continue to have different device pointers than their host pointers, so cudaHostGetDevicePointer() remains necessary in that case. The NVIDIA Ampere GPU architecture increases the capacity of the L2 cache to 40 MB in Tesla A100, which is 7x larger than Tesla V100. As a result, this section discusses size but not dimension. For some applications the problem size will remain constant and hence only strong scaling is applicable. 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. 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. Here, the effective bandwidth is in units of GB/s, Br is the number of bytes read per kernel, Bw is the number of bytes written per kernel, and time is given in seconds. Along with the increased memory capacity, the bandwidth is increased by 72%, from 900 GB/s on Volta V100 to 1550 GB/s on A100. 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. If such an application is run on a system with the R418 driver installed, CUDA initialization will return an error as can be seen in the example below. 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. For example, consider the following code: Here, the sub-expression stride*i could overflow a 32-bit integer, so if i is declared as unsigned, the overflow semantics prevent the compiler from using some optimizations that might otherwise have applied, such as strength reduction. Understanding the Programming Environment, 15. We can see this usage in the following example: NVRTC is a runtime compilation library for CUDA C++. On devices of compute capability 6.0 or higher, L1-caching is the default, however the data access unit is 32-byte regardless of whether global loads are cached in L1 or not. Sample CUDA configuration data reported by deviceQuery. These memory spaces include global, local, shared, texture, and registers, as shown in Figure 2. For example, the ability to overlap kernel execution with asynchronous data transfers between the host and the device is available on most but not all GPUs irrespective of the compute capability. For more information on this pragma, refer to the CUDA C++ Programming Guide. On devices with GDDR memory, accessing memory in a coalesced way is even more important when ECC is turned on. This number is divided by the time in seconds to obtain GB/s. 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. On integrated GPUs (i.e., GPUs with the integrated field of the CUDA device properties structure set to 1), mapped pinned memory is always a performance gain because it avoids superfluous copies as integrated GPU and CPU memory are physically the same. The PTX string generated by NVRTC can be loaded by cuModuleLoadData and cuModuleLoadDataEx. Many software libraries and applications built on top of CUDA (e.g. Throughout this guide, Kepler refers to devices of compute capability 3.x, Maxwell refers to devices of compute capability 5.x, Pascal refers to device of compute capability 6.x, Volta refers to devices of compute capability 7.0, Turing refers to devices of compute capability 7.5, and NVIDIA Ampere GPU Architecture refers to devices of compute capability 8.x. Its result will often differ slightly from results obtained by doing the two operations separately. --ptxas-options=-v or -Xptxas=-v lists per-kernel register, shared, and constant memory usage. (Developers targeting a single machine with known configuration may choose to skip this section.). For example, improving occupancy from 66 percent to 100 percent generally does not translate to a similar increase in performance. Figure 6 illustrates how threads in the CUDA device can access the different memory components. This is evident from the saw tooth curves. On the other hand, some applications designs will require some amount of refactoring to expose their inherent parallelism. Using these data items, the peak theoretical memory bandwidth of the NVIDIA Tesla V100 is 898 GB/s: \(\left. All threads within one block see the same shared memory array . Having a semantically versioned ABI means the interfaces need to be maintained and versioned. These bindings expose the same features as the C-based interface and also provide backwards compatibility. The way to avoid strided access is to use shared memory as before, except in this case a warp reads a row of A into a column of a shared memory tile, as shown in An optimized handling of strided accesses using coalesced reads from global memory. Minimize data transfers between the host and the device. 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. 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. (For further information, refer to Performance Guidelines in the CUDA C++ Programming Guide). Unified Shared Memory/L1/Texture Cache, NVIDIA Ampere GPU Architecture Compatibility Guide for CUDA Applications. Thedriver will honor the specified preference except when a kernel requires more shared memory per thread block than available in the specified configuration. Execution Configuration Optimizations, 11.1.2. Code that uses the warp shuffle operation, for example, must be compiled with -arch=sm_30 (or higher compute capability). Therefore, the compiler can optimize more aggressively with signed arithmetic than it can with unsigned arithmetic. Always check the error return values on all CUDA API functions, even for functions that are not expected to fail, as this will allow the application to detect and recover from errors as soon as possible should they occur. We evaluate the performance of both kernels using elements of size 4B, 8B and 16B per thread i.e., using int, int2 and int4 for the template parameter. An application can also use the Occupancy API from the CUDA Runtime, e.g. 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). It is also the only way for applications to run on devices that did not exist at the time the application was compiled. In both cases, kernels must be compiled into binary code by nvcc (called cubins) to execute on the device. For example, if you link against the CUDA 11.1 dynamic runtime, and use functionality from 11.1, as well as a separate shared library that was linked against the CUDA 11.2 dynamic runtime that requires 11.2 functionality, the final link step must include a CUDA 11.2 or newer dynamic runtime. 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. See Version Management for details on how to query the available CUDA software API versions. The host runtime component of the CUDA software environment can be used only by host functions. If B has not finished writing its element before A tries to read it, we have a race condition, which can lead to undefined behavior and incorrect results. Minimize redundant accesses to global memory whenever possible. 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. 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. The same goes for other CUDA Toolkit libraries: cuFFT has an interface similar to that of FFTW, etc. The access policy window requires a value for hitRatio and num_bytes. If sequential threads in a warp access memory that is sequential but not aligned with a 32-byte segment, five 32-byte segments will be requested, as shown in Figure 4. The NVIDIA Ampere GPU architecture adds hardware acceleration for copying data from global memory to shared memory. The use of shared memory is illustrated via the simple example of a matrix multiplication C = AB for the case with A of dimension Mxw, B of dimension wxN, and C of dimension MxN. With the use of shared memory we can fetch data from global memory and place it into on-chip memory with far lower latency and higher bandwidth then global memory. For the NVIDIA Tesla V100, global memory accesses with no offset or with offsets that are multiples of 8 words result in four 32-byte transactions. 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. See the CUDA C++ Programming Guide for further explanations and software requirements for UVA and P2P. The warp size is 32 threads and the number of banks is also 32, so bank conflicts can occur between any threads in the warp. The high-priority recommendations from those guides are as follows: Find ways to parallelize sequential code. Assess, Parallelize, Optimize, Deploy, 3.1.3.1. 2) In one block I need to load into shared memory the queues of other blocks. No contractual obligations are formed either directly or indirectly by this document. The two kernels are very similar, differing only in how the shared memory arrays are declared and how the kernels are invoked. Unified memory: supports seamless access to buffers or objects from multiple GPUs and CPUs. As seen above, in the case of misaligned sequential accesses, caches help to alleviate the performance impact. However, the device is based on a distinctly different design from the host system, and its important to understand those differences and how they determine the performance of CUDA applications in order to use CUDA effectively. 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. Built on top of these technologies are CUDA libraries, some of which are included in the CUDA Toolkit, while others such as cuDNN may be released independently of the CUDA Toolkit. Using the CUDA Occupancy Calculator to project GPU multiprocessor occupancy. Find centralized, trusted content and collaborate around the technologies you use most. The versions of the components in the toolkit are available in this table. Asynchronous transfers enable overlap of data transfers with computation in two different ways. They can be distinguished by their names: some have names with prepended underscores, whereas others do not (e.g., __functionName() versus functionName()). See Compute Capability 5.x in the CUDA C++ Programming Guide for further details. Armed with this knowledge, the developer can evaluate these bottlenecks for parallelization and start to investigate GPU acceleration. C++-style convenience wrappers (cuda_runtime.h) built on top of the C-style functions. If not, my suggestion would be to start by breaking your work into separate kernels, and using the kernel launch(es) as sync points. Asynchronous Copy from Global Memory to Shared Memory CUDA 11.0 introduces an async-copy feature that can be used within device code . Use of such information may require a license from a third party under the patents or other intellectual property rights of the third party, or a license from NVIDIA under the patents or other intellectual property rights of NVIDIA. 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. Another important concept is the management of system resources allocated for a particular task. In our experiment, we vary the size of this persistent data region from 10 MB to 60 MB to model various scenarios where data fits in or exceeds the available L2 set-aside portion of 30 MB. For example, in the standard CUDA Toolkit installation, the files libcublas.so and libcublas.so.5.5 are both symlinks pointing to a specific build of cuBLAS, which is named like libcublas.so.5.5.x, where x is the build number (e.g., libcublas.so.5.5.17). On Linux and Mac, the -rpath linker option should be used to instruct the executable to search its local path for these libraries before searching the system paths: It may be necessary to adjust the value of -ccbin to reflect the location of your Visual Studio installation. So, if each thread block uses many registers, the number of thread blocks that can be resident on a multiprocessor is reduced, thereby lowering the occupancy of the multiprocessor. This does not apply to the NVIDIA Driver; the end user must still download and install an NVIDIA Driver appropriate to their GPU(s) and operating system. Asynchronous Data Copy from Global Memory to Shared Memory, 1.4.1.3. The current board power draw and power limits are reported for products that report these measurements. Handling New CUDA Features and Driver APIs, 15.4.1.4. Using shared memory to coalesce global reads. CUDA Binary (cubin) Compatibility, 15.4. Details about occupancy are displayed in the Occupancy section. 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. Data should be kept on the device as long as possible. Overlapping computation and data transfers. This advantage is increased when several powers of the same base are needed (e.g., where both x2 and x5 are calculated in close proximity), as this aids the compiler in its common sub-expression elimination (CSE) optimization. Functions following functionName() naming convention are slower but have higher accuracy (e.g., sinf(x) and expf(x)). Reproduction of information in this document is permissible only if approved in advance by NVIDIA in writing, reproduced without alteration and in full compliance with all applicable export laws and regulations, and accompanied by all associated conditions, limitations, and notices. Memory optimizations are the most important area for performance. 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. Kernels can be written using the CUDA instruction set architecture, called PTX, which is described in the PTX reference manual.
