For a warp of threads, col represents sequential columns of the transpose of A, and therefore col*TILE_DIM represents a strided access of global memory with a stride of w, resulting in plenty of wasted bandwidth. Strong Scaling and Amdahls Law describes strong scaling, which allows us to set an upper bound for the speedup with a fixed problem size. Adjacent threads accessing memory with a stride of 2. Functions following the __functionName() naming convention map directly to the hardware level. Within a kernel call, the texture cache is not kept coherent with respect to global memory writes, so texture fetches from addresses that have been written via global stores in the same kernel call return undefined data. I think this pretty much implies that you are going to have the place the heads of each queue in global memory. As a useful side effect, this strategy will allow us a means to reduce code duplication should we wish to include both CPU and GPU execution paths in our application: if the bulk of the work of our CUDA kernels is done in __host__ __device__ functions, we can easily call those functions from both the host code and the device code without duplication. For example, a matrix multiplication of the same matrices requires N3 operations (multiply-add), so the ratio of operations to elements transferred is O(N), in which case the larger the matrix the greater the performance benefit. CUDA devices use several memory spaces, which have different characteristics that reflect their distinct usages in CUDA applications. The performance of the above kernel is shown in the chart below. 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. The context is explicit in the CUDA Driver API but is entirely implicit in the CUDA Runtime API, which creates and manages contexts automatically. 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). By describing your computation in terms of these high-level abstractions you provide Thrust with the freedom to select the most efficient implementation automatically. 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. No. Is it known that BQP is not contained within NP? Concurrent copy and execute demonstrates how to overlap kernel execution with asynchronous data transfer. For example, improving occupancy from 66 percent to 100 percent generally does not translate to a similar increase in performance. An additional set of Perl and Python bindings are provided for the NVML API. Weaknesses in customers product designs may affect the quality and reliability of the NVIDIA product and may result in additional or different conditions and/or requirements beyond those contained in this document. All rights reserved. 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). Both pow() and powf() are heavy-weight functions in terms of register pressure and instruction count due to the numerous special cases arising in general exponentiation and the difficulty of achieving good accuracy across the entire ranges of the base and the exponent. Note that when a thread block allocates more registers than are available on a multiprocessor, the kernel launch fails, as it will when too much shared memory or too many threads are requested. rev2023.3.3.43278. 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. Details about occupancy are displayed in the Occupancy section. In calculating each of the rows of a tile of matrix C, the entire tile of B is read. Distributing the CUDA Runtime and Libraries, 16.4.1. Therefore, an application that compiled successfully on an older version of the toolkit may require changes in order to compile against a newer version of the toolkit. Loop Counters Signed vs. Unsigned, 11.1.5. Creating additional contexts incurs memory overhead for per-context data and time overhead for context switching. Optimizing memory usage starts with minimizing data transfers between the host and the device because those transfers have much lower bandwidth than internal device data transfers. This is particularly beneficial to kernels that frequently call __syncthreads(). Performance benefits can be more readily achieved when this ratio is higher. 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. Alternatively, NVRTC can generate cubins directly starting with CUDA 11.1. These instructions also avoid using extra registers for memory copies and can also bypass the L1 cache. 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. Not requiring driver updates for new CUDA releases can mean that new versions of the software can be made available faster to users. No license, either expressed or implied, is granted under any NVIDIA patent right, copyright, or other NVIDIA intellectual property right under this document. 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. NVIDIA GPU device driver - Kernel-mode driver component for NVIDIA GPUs. Furthermore, the pinning of system memory is a heavyweight operation compared to most normal system memory allocations, so as with all optimizations, test the application and the systems it runs on for optimal performance parameters. With each generation of NVIDIA processors, new features are added to the GPU that CUDA can leverage. Starting with CUDA 11, the toolkit versions are based on an industry-standard semantic versioning scheme: .X.Y.Z, where: .X stands for the major version - APIs have changed and binary compatibility is broken. By leveraging the semantic versioning, starting with CUDA 11, components in the CUDA Toolkit will remain binary compatible across the minor versions of the toolkit. Managing your GPU cluster will help achieve maximum GPU utilization and help you and your users extract the best possible performance. This also prevents array elements being repeatedly read from global memory if the same data is required several times. As you have correctly said, if only one block fits per SM because of the amount of shared memory used, only one block will be scheduled at any one time. At a minimum, you would need some sort of selection process that can access the heads of each queue. For optimal performance, users should manually tune the NUMA characteristics of their application. Certain hardware features are not described by the compute capability. As a result, a read from constant memory costs one memory read from device memory only on a cache miss; otherwise, it just costs one read from the constant cache. Applying Strong and Weak Scaling, 6.3.2. The number of copy engines on a GPU is given by the asyncEngineCount field of the cudaDeviceProp structure, which is also listed in the output of the deviceQuery CUDA Sample. Any PTX device code loaded by an application at runtime is compiled further to binary code by the device driver. 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. If the GPU must wait on one warp of threads, it simply begins executing work on another. An optimized handling of strided accesses using coalesced reads from global memory. Notwithstanding any damages that customer might incur for any reason whatsoever, NVIDIAs aggregate and cumulative liability towards customer for the products described herein shall be limited in accordance with the Terms of Sale for the product. Strong scaling is a measure of how, for a fixed overall problem size, the time to solution decreases as more processors are added to a system. A pointer to a structure with a size embedded is a better solution. Even though each multiprocessor contains thousands of 32-bit registers (see Features and Technical Specifications of the CUDA C++ Programming Guide), these are partitioned among concurrent threads. Understanding the Programming Environment, 15. Many codes accomplish a significant portion of the work with a relatively small amount of code. Randomly accessing. As with the dynamically-linked version of the CUDA Runtime library, these libraries should be bundled with the application executable when distributing that application. On Systems on a Chip with integrated GPUs, such as NVIDIA Tegra, host and device memory are physically the same, but there is still a logical distinction between host and device memory. The bandwidthTest CUDA Sample shows how to use these functions as well as how to measure memory transfer performance. Like the other calls in this listing, their specific operation, parameters, and return values are described in the CUDA Toolkit Reference Manual. More information on cubins, PTX and application compatibility can be found in the CUDA C++ Programming Guide. Inspection of the PTX assembly code (obtained by compiling with -ptx or -keep command-line options to nvcc) reveals whether a variable has been placed in local memory during the first compilation phases. Floating Point Math Is not Associative, 8.2.3. No contractual obligations are formed either directly or indirectly by this document. However, since APOD is a cyclical process, we might opt to parallelize these functions in a subsequent APOD pass, thereby limiting the scope of our work in any given pass to a smaller set of incremental changes. As an exception, scattered writes to HBM2 see some overhead from ECC but much less than the overhead with similar access patterns on ECC-protected GDDR5 memory. If this set-aside portion is not used by persistent accesses, then streaming or normal data accesses can use it. nvidia-smi is targeted at Tesla and certain Quadro GPUs, though limited support is also available on other NVIDIA GPUs. Overall, developers can expect similar occupancy as on Volta without changes to their application. Consequently, its important to understand the characteristics of the architecture. Lets assume that A and B are threads in two different warps. However, it also can act as a constraint on occupancy. 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. outside your established ABI contract. This is important for a number of reasons; for example, it allows the user to profit from their investment as early as possible (the speedup may be partial but is still valuable), and it minimizes risk for the developer and the user by providing an evolutionary rather than revolutionary set of changes to the application. By understanding how applications can scale it is possible to set expectations and plan an incremental parallelization strategy. A natural decomposition of the problem is to use a block and tile size of wxw threads. Applications compiled against a CUDA Toolkit version will only run on systems with the specified minimum driver version for that toolkit version. Although each of these instructions is scheduled for execution, only the instructions with a true predicate are actually executed. 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. vegan) just to try it, does this inconvenience the caterers and staff? For Windows, the /DELAY option is used; this requires that the application call SetDllDirectory() before the first call to any CUDA API function in order to specify the directory containing the CUDA DLLs. 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. The CUDA Toolkit libraries (cuBLAS, cuFFT, etc.) (To determine the latter number, see the deviceQuery CUDA Sample or refer to Compute Capabilities in the CUDA C++ Programming Guide.) More details about the compute capabilities of various GPUs are in CUDA-Enabled GPUs and Compute Capabilities of the CUDA C++ Programming Guide. Minimize redundant accesses to global memory whenever possible. The performance of the kernels is shown in Figure 14. Where to Install Redistributed CUDA Libraries, 17.4. Is it possible to create a concave light? Do new devs get fired if they can't solve a certain bug? 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. Applications compiled with CUDA toolkit versions as old as 3.2 will run on newer drivers. Does there exist a square root of Euler-Lagrange equations of a field? Pinned memory is allocated using the cudaHostAlloc() functions in the Runtime API. Just-in-time compilation increases application load time but allows applications to benefit from latest compiler improvements. 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. 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 ensures your code is compatible. However, compared to cache based architectures, like CPUs, latency hiding architectures, like GPUs, tend to cope better with completely random memory access patterns. When an application depends on the availability of certain hardware or software capabilities to enable certain functionality, the CUDA API can be queried for details about the configuration of the available device and for the installed software versions. These bindings expose the same features as the C-based interface and also provide backwards compatibility. Prior to CUDA 11.0, the minimum driver version for a toolkit was the same as the driver shipped with that version of the CUDA Toolkit. After the application is dynamically linked against the CUDA Runtime, this version of the runtime library should be bundled with the application. This information is obtained by calling cudaGetDeviceProperties() and accessing the information in the structure it returns. In fact, shared memory latency is roughly 100x lower than uncached global memory latency (provided that there are no bank conflicts between the threads, which we will examine later in this post). 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. We define source compatibility as a set of guarantees provided by the library, where a well-formed application built against a specific version of the library (using the SDK) will continue to build and run without errors when a newer version of the SDK is installed. nvidia-smi ships with NVIDIA GPU display drivers on Linux, and with 64-bit Windows Server 2008 R2 and Windows 7. nvidia-smi can output queried information as XML or as human-readable plain text either to standard output or to a file. For example, if the hitRatio value is 0.6, 60% of the memory accesses in the global memory region [ptr..ptr+num_bytes) have the persisting property and 40% of the memory accesses have the streaming property. The compiler will perform these conversions if n is literal. Making statements based on opinion; back them up with references or personal experience. For devices of compute capability 6.0 or higher, the requirements can be summarized quite easily: the concurrent accesses of the threads of a warp will coalesce into a number of transactions equal to the number of 32-byte transactions necessary to service all of the threads of the warp. 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. 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. 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. Since shared memory is shared amongst threads in a thread block, it provides a mechanism for threads to cooperate. The CUDA driver ensures backward Binary Compatibility is maintained for compiled CUDA applications. 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. Staged concurrent copy and execute shows how the transfer and kernel execution can be broken up into nStreams stages. It presents established parallelization and optimization techniques and explains coding metaphors and idioms that can greatly simplify programming for CUDA-capable GPU architectures. Tuning the Access Window Hit-Ratio, 9.2.3.2. The kernel is executed within a loop in host code that varies the parameter offset from 0 to 32. 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. Compatibility of the CUDA platform is thus intended to address a few scenarios: NVIDIA driver upgrades to systems with GPUs running in production for enterprises or datacenters can be complex and may need advance planning. Kernels can be written using the CUDA instruction set architecture, called PTX, which is described in the PTX reference manual. The CUDA software environment consists of three parts: CUDA Toolkit (libraries, CUDA runtime and developer tools) - SDK for developers to build CUDA applications. The NVIDIA A100 GPU based on compute capability 8.0 increases the maximum capacity of the combined L1 cache, texture cache and shared memory to 192 KB, 50% larger than the L1 cache in NVIDIA V100 GPU. Unified Shared Memory/L1/Texture Cache, NVIDIA Ampere GPU Architecture Compatibility Guide for CUDA Applications. Now Let's Look at Shared Memory Common Programming Pattern (5.1.2 of CUDA manual) - Load data into shared memory - Synchronize (if necessary) - Operate on data in shared memory - Synchronize (if necessary) - Write intermediate results to global memory - Repeat until done Shared memory Global memory Familiar concept?? No contractual obligations are formed either directly or indirectly by this document. This guide refers to and relies on several other documents that you should have at your disposal for reference, all of which are available at no cost from the CUDA website https://docs.nvidia.com/cuda/. The CUDA Toolkits End-User License Agreement (EULA) allows for redistribution of many of the CUDA libraries under certain terms and conditions. TF32 is a new 19-bit Tensor Core format that can be easily integrated into programs for more accurate DL training than 16-bit HMMA formats. Testing of all parameters of each product is not necessarily performed by NVIDIA. (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.). The compiler optimizes 1.0f/sqrtf(x) into rsqrtf() only when this does not violate IEEE-754 semantics. This can be used to manage data caches, speed up high-performance cooperative parallel algorithms, and facilitate global memory coalescing in cases where it would otherwise not be possible. Production code should, however, systematically check the error code returned by each API call and check for failures in kernel launches by calling cudaGetLastError(). Using Kolmogorov complexity to measure difficulty of problems? Because shared memory is shared by threads in a thread block, it provides a mechanism for threads to cooperate. The synchronous version for the kernel loads an element from global memory to an intermediate register and then stores the intermediate register value to shared memory. 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. It will not allow any other CUDA call to begin until it has completed.) PTX programs are translated at load time to the target hardware instruction set via the JIT Compiler which is part of the CUDA driver. If you want to communicate (i.e. Shared memory is allocated per thread block, so all threads in the block have access to the same shared memory. However, the set of registers (known as the register file) is a limited commodity that all threads resident on a multiprocessor must share. Zero copy can be used in place of streams because kernel-originated data transfers automatically overlap kernel execution without the overhead of setting up and determining the optimal number of streams. 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. 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. 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. The NVIDIA Ampere GPU architecture adds native support for warp wide reduction operations for 32-bit signed and unsigned integer operands. C++-style convenience wrappers (cuda_runtime.h) built on top of the C-style functions. Another important concept is the management of system resources allocated for a particular task. 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. 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. In the previous post, I looked at how global memory accesses by a group of threads can be coalesced into a single transaction, and howalignment and stride affect coalescing for various generations of CUDA hardware. (The performance advantage sinpi() has over sin() is due to simplified argument reduction; the accuracy advantage is because sinpi() multiplies by only implicitly, effectively using an infinitely precise mathematical rather than a single- or double-precision approximation thereof.). 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.