On devices that have this capability, the overlap once again requires pinned host memory, and, in addition, the data transfer and kernel must use different, non-default streams (streams with non-zero stream IDs). To execute any CUDA program, there are three main steps: Copy the input data from host memory to device memory, also known as host-to-device transfer. Threads on a CPU are generally heavyweight entities. Customer should obtain the latest relevant information before placing orders and should verify that such information is current and complete. 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. This makes the code run faster at the cost of diminished precision and accuracy. This is called just-in-time compilation (JIT). The details of various CPU timing approaches are outside the scope of this document, but developers should always be aware of the resolution their timing calls provide. NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any time without notice. 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. In CUDA only threads and the host can access memory. 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 major and minor revision numbers of the compute capability are shown on the seventh line of Figure 16. Threads can access data in shared memory loaded from global memory by other threads within the same thread block. On all CUDA-enabled devices, it is possible to overlap host computation with asynchronous data transfers and with device computations. With each generation of NVIDIA processors, new features are added to the GPU that CUDA can leverage. Load the GPU program and execute, caching data on-chip for performance. These bindings expose the same features as the C-based interface and also provide backwards compatibility. For example, a 64-bit application linked to cuBLAS 5.5 will look for cublas64_55.dll at runtime, so this is the file that should be redistributed with that application, even though cublas.lib is the file that the application is linked against. Shared memory is allocated per thread block, so all threads in the block have access to the same shared memory. The maximum number of registers per thread is 255. In general, they should be avoided, because compared to peak capabilities any architecture processes these memory access patterns at a low efficiency. If instead i is declared as signed, where the overflow semantics are undefined, the compiler has more leeway to use these optimizations. One method for doing so utilizes shared memory, which is discussed in the next section. -use_fast_math compiler option of nvcc coerces every functionName() call to the equivalent __functionName() call. The compiler must on occasion insert conversion instructions, introducing additional execution cycles. if several threads had accessed the same word or if some threads did not participate in the access), the full segment is fetched anyway. Failure to do so could lead to too many resources requested for launch errors. To do so, use this equation: \(\text{Effective\ bandwidth} = \left( {\left( B_{r} + B_{w} \right) \div 10^{9}} \right) \div \text{time}\). Hence, for best overall application performance, it is important to minimize data transfer between the host and the device, even if that means running kernels on the GPU that do not demonstrate any speedup compared with running them on the host CPU. To verify the exact DLL filename that the application expects to find at runtime, use the dumpbin tool from the Visual Studio command prompt: Once the correct library files are identified for redistribution, they must be configured for installation into a location where the application will be able to find them. 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. Recommendations for taking advantage of minor version compatibility in your application, 16.4. Useful Features for tex1D(), tex2D(), and tex3D() Fetches, __launch_bounds__(maxThreadsPerBlock,minBlocksPerMultiprocessor), Using the CUDA Occupancy Calculator to project GPU multiprocessor occupancy, cudaOccupancyMaxActiveBlocksPerMultiprocessor, // When the program/library launches work, // When the program/library is finished with the context, Table 5. Support for TF32 Tensor Core, through HMMA instructions. For exponentiation using base 2 or 10, use the functions exp2() or expf2() and exp10() or expf10() rather than the functions pow() or powf(). How to notate a grace note at the start of a bar with lilypond? In fact, local memory is off-chip. This allows applications that depend on these libraries to redistribute the exact versions of the libraries against which they were built and tested, thereby avoiding any trouble for end users who might have a different version of the CUDA Toolkit (or perhaps none at all) installed on their machines. This is advantageous with regard to both accuracy and performance. Note that the performance improvement is not due to improved coalescing in either case, but to avoiding redundant transfers from global memory. 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. If the PTX is also not available, then the kernel launch will fail. . By simply increasing this parameter (without modifying the kernel), it is possible to effectively reduce the occupancy of the kernel and measure its effect on performance. For each iteration i of the for loop, the threads in a warp read a row of the B tile, which is a sequential and coalesced access for all compute capabilities. In order to maintain binary compatibility across minor versions, the CUDA runtime no longer bumps up the minimum driver version required for every minor release - this only happens when a major release is shipped. The cudaChooseDevice() function can be used to select the device that most closely matches a desired set of features. 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). This limitation is not specific to CUDA, but an inherent part of parallel computation on floating-point values. The cause of the difference is shared memory bank conflicts. 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. The compiler and hardware thread scheduler will schedule instructions as optimally as possible to avoid register memory bank conflicts. 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 only performance issue with shared memory is bank conflicts, which we will discuss later. Warp level support for Reduction Operations, 1.4.2.1. The throughput of __sinf(x), __cosf(x), and__expf(x) is much greater than that of sinf(x), cosf(x), and expf(x). A key concept in this effort is occupancy, which is explained in the following sections. NVRTC used to support only virtual architectures through the option -arch, since it was only emitting PTX. To enable the loads from global memory to be coalesced, data are read from global memory sequentially. 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. Register dependencies arise when an instruction uses a result stored in a register written by an instruction before it. 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?? 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/. In these cases, no warp can ever diverge. Block-column matrix multiplied by block-row matrix. This can be configured during runtime API from the host for all kernelsusing cudaDeviceSetCacheConfig() or on a per-kernel basis using cudaFuncSetCacheConfig(). For some applications the problem size will remain constant and hence only strong scaling is applicable. There are many such factors involved in selecting block size, and inevitably some experimentation is required. 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 CUDA Toolkits End-User License Agreement (EULA) allows for redistribution of many of the CUDA libraries under certain terms and conditions. CUDA Memory Rules Currently can only transfer data from host to global (and constant memory) and not host directly to shared. Like the other calls in this listing, their specific operation, parameters, and return values are described in the CUDA Toolkit Reference Manual. In order to maintain forward compatibility to future hardware and toolkits and to ensure that at least one thread block can run on an SM, developers should include the single argument __launch_bounds__(maxThreadsPerBlock) which specifies the largest block size that the kernel will be launched with. Concurrent copy and execute demonstrates how to overlap kernel execution with asynchronous data transfer. Block-column matrix (A) multiplied by block-row matrix (B) with resulting product matrix (C).. 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. Applications compiled against a CUDA Toolkit version will only run on systems with the specified minimum driver version for that toolkit version. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. The CUDA Runtime handles kernel loading and setting up kernel parameters and launch configuration before the kernel is launched. Asking for help, clarification, or responding to other answers. See Register Pressure. Instead, all instructions are scheduled, but a per-thread condition code or predicate controls which threads execute the instructions. The cubins are architecture-specific. However, if multiple addresses of a memory request map to the same memory bank, the accesses are serialized. It may be different with non-unit-strided accesses, however, and this is a pattern that occurs frequently when dealing with multidimensional data or matrices. A copy kernel that illustrates misaligned accesses. \left( 0.877 \times 10^{9} \right. Performance benefits can be more readily achieved when this ratio is higher. 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. In a typical system, thousands of threads are queued up for work (in warps of 32 threads each). 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. Details about occupancy are displayed in the Occupancy section. For example, on devices of compute capability 7.0 each multiprocessor has 65,536 32-bit registers and can have a maximum of 2048 simultaneous threads resident (64 warps x 32 threads per warp). (The exceptions to this are kernel launches, which return void, and cudaGetErrorString(), which returns a character string describing the cudaError_t code that was passed into it.) 1) I need to select only k blocks of out m blocks whose heads of queue is minimum k elements out of m elements. Performance Improvements Optimizing C = AB Matrix Multiply
For this purpose, it requires mapped pinned (non-pageable) memory. Recall that shared memory is local to each SM. This padding eliminates the conflicts entirely, because now the stride between threads is w+1 banks (i.e., 33 for current devices), which, due to modulo arithmetic used to compute bank indices, is equivalent to a unit stride. See the CUDA C++ Programming Guide for details. Missing dependencies is also a binary compatibility break, hence you should provide fallbacks or guards for functionality that depends on those interfaces. There are a number of tools that can be used to generate the profile. If you preorder a special airline meal (e.g. Using asynchronous copies does not use any intermediate register. read- only by GPU) Shared memory is said to provide up to 15x speed of global memory Registers have similar speed to shared memory if reading same address or no bank conicts. 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. This access pattern results in four 32-byte transactions, indicated by the red rectangles. As for optimizing instruction usage, the use of arithmetic instructions that have low throughput should be avoided. Medium Priority: Prefer faster, more specialized math functions over slower, more general ones when possible. 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. In the case of texture access, if a texture reference is bound to a linear array in global memory, then the device code can write to the underlying array. Figure 6 illustrates how threads in the CUDA device can access the different memory components. It presents established parallelization and optimization techniques and explains coding metaphors and idioms that can greatly simplify programming for CUDA-capable GPU architectures. Throughput Reported by Visual Profiler, 9.1. Programmers must primarily focus on following those recommendations to achieve the best performance. nvidia-smi is targeted at Tesla and certain Quadro GPUs, though limited support is also available on other NVIDIA GPUs. 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. With wrap, x is replaced by frac(x) where frac(x) = x - floor(x). 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. In Overlapping computation and data transfers, the memory copy and kernel execution occur sequentially. Because execution within a stream occurs sequentially, none of the kernels will launch until the data transfers in their respective streams complete. To scale to future devices, the number of blocks per kernel launch should be in the thousands. Clear single-bit and double-bit ECC error counts. 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. Such a pattern is shown in Figure 3. (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.). High Priority: Minimize the use of global memory. 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. See the nvidia-smi documenation for details. The access policy window requires a value for hitRatio and num_bytes. 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). x86 processors can use an 80-bit double extended precision math when performing floating-point calculations. 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. A device in which work is poorly balanced across the multiprocessors will deliver suboptimal performance. The effective bandwidth for this kernel is 12.8 GB/s on an NVIDIA Tesla V100. 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. A CUDA context is a software environment that manages memory and other resources If we validate our addressing logic separately prior to introducing the bulk of the computation, then this will simplify any later debugging efforts. How do I align things in the following tabular environment? NVIDIA makes no representation or warranty that products based on this document will be suitable for any specified use. A slightly related but important topic is one of application binary compatibility across GPU architectures in CUDA. A portion of the L2 cache can be set aside for persistent accesses to a data region in global memory. 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. and one element in the streaming data section. Zero copy is a feature that was added in version 2.2 of the CUDA Toolkit. Can airtags be tracked from an iMac desktop, with no iPhone? When accessing uncached local or global memory, there are hundreds of clock cycles of memory latency. Is it possible to create a concave light? Many codes accomplish a significant portion of the work with a relatively small amount of code. The CUDA Toolkit includes a number of such libraries that have been fine-tuned for NVIDIA CUDA GPUs, such as cuBLAS, cuFFT, and so on. This means that in one of these devices, for a multiprocessor to have 100% occupancy, each thread can use at most 32 registers. For more details refer to the memcpy_async section in the CUDA C++ Programming Guide. .Z stands for the release/patch version - new updates and patches will increment this. On parallel systems, it is possible to run into difficulties not typically found in traditional serial-oriented programming. For more information on this pragma, refer to the CUDA C++ Programming Guide. What sort of strategies would a medieval military use against a fantasy giant? The following sections discuss some caveats and considerations. For other algorithms, implementations may be considered correct if they match the reference within some small epsilon. This is evident from the saw tooth curves. As a result, it is recommended that first-time readers proceed through the guide sequentially. Global memory: is the memory residing graphics/accelerator card but not inside GPU chip. All CUDA compute devices follow the IEEE 754 standard for binary floating-point representation, with some small exceptions. 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 simple remedy is to pad the shared memory array so that it has an extra column, as in the following line of code. Dont expose ABI structures that can change. One way to use shared memory that leverages such thread cooperation is to enable global memory coalescing, as demonstrated by the array reversal in this post. A stream is simply a sequence of operations that are performed in order on the device. A trivial example is when the controlling condition depends only on (threadIdx / WSIZE) where WSIZE is the warp size. But since any repeated access to such memory areas causes repeated CPU-GPU transfers, consider creating a second area in device memory to manually cache the previously read host memory data. Note that the process used for validating numerical results can easily be extended to validate performance results as well. To prevent the compiler from allocating too many registers, use the -maxrregcount=N compiler command-line option (see nvcc) or the launch bounds kernel definition qualifier (see Execution Configuration of the CUDA C++ Programming Guide) to control the maximum number of registers to allocated per thread. Production code should, however, systematically check the error code returned by each API call and check for failures in kernel launches by calling cudaGetLastError(). Setting the bank size to eight bytes can help avoid shared memory bank conflicts when accessing double precision data. The access requirements for coalescing depend on the compute capability of the device and are documented in the CUDA C++ Programming Guide. There are multiple ways to declare shared memory inside a kernel, depending on whether the amount of memory is known at compile time or at run time. Your code might reflect different priority factors. Copy the results from device memory to host memory, also called device-to-host transfer. This also prevents array elements being repeatedly read from global memory if the same data is required several times. These recommendations are categorized by priority, which is a blend of the effect of the recommendation and its scope. It also disables single-precision denormal support and lowers the precision of single-precision division in general. Shared memory accesses, in counterpoint, are usually worth optimizing only when there exists a high degree of bank conflicts. In the next post I will continue our discussion of shared memory by using it to optimize a matrix transpose. More details about the compute capabilities of various GPUs are in CUDA-Enabled GPUs and Compute Capabilities of the CUDA C++ Programming Guide. Testing of all parameters of each product is not necessarily performed by NVIDIA. Incorrect or unexpected results arise principally from issues of floating-point accuracy due to the way floating-point values are computed and stored. Furthermore, register allocations are rounded up to the nearest 256 registers per warp. Each component in the toolkit is recommended to be semantically versioned. When deploying a CUDA application, it is often desirable to ensure that the application will continue to function properly even if the target machine does not have a CUDA-capable GPU and/or a sufficient version of the NVIDIA Driver installed. It provides functions to handle the following: Interoperability with OpenGL and Direct3D. The NVIDIA Ampere GPU architecture adds native support for warp wide reduction operations for 32-bit signed and unsigned integer operands. Memory instructions include any instruction that reads from or writes to shared, local, or global memory. On the other hand, some applications designs will require some amount of refactoring to expose their inherent parallelism. But this technique is still useful for other access patterns, as Ill show in the next post.). CUDA programming involves running code on two different platforms concurrently: a host system with one or more CPUs and one or more CUDA-enabled NVIDIA GPU devices. Code that uses the warp shuffle operation, for example, must be compiled with -arch=sm_30 (or higher compute capability). Memory Access 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. The following throughput metrics can be displayed in the Details or Detail Graphs view: The Requested Global Load Throughput and Requested Global Store Throughput values indicate the global memory throughput requested by the kernel and therefore correspond to the effective bandwidth obtained by the calculation shown under Effective Bandwidth Calculation. These results are substantially lower than the corresponding measurements for the C = AB kernel. 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 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. GPUs with a single copy engine can perform one asynchronous data transfer and execute kernels whereas GPUs with two copy engines can simultaneously perform one asynchronous data transfer from the host to the device, one asynchronous data transfer from the device to the host, and execute kernels. Accesses to the remaining data of the memory region (i.e., streaming data) are considered normal or streaming accesses and will thus use the remaining 10 MB of the non set-aside L2 portion (unless part of the L2 set-aside portion is unused). It is easy and informative to explore the ramifications of misaligned accesses using a simple copy kernel, such as the one in A copy kernel that illustrates misaligned accesses. As mentioned in Occupancy, higher occupancy does not always equate to better performance. An exception is the case where all threads in a warp address the same shared memory address, resulting in a broadcast. Furthermore, this file should be installed into the @rpath of the application; see Where to Install Redistributed CUDA Libraries. There is a total of 64 KB constant memory on a device. Shared memory is specified by the device architecture and is measured on per-block basis. No. Accesses to different addresses by threads within a warp are serialized, thus the cost scales linearly with the number of unique addresses read by all threads within a warp. BFloat16 format is especially effective for DL training scenarios. 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. Another important concept is the management of system resources allocated for a particular task. However, low occupancy always interferes with the ability to hide memory latency, resulting in performance degradation. The NVIDIA Ampere GPU architectures Streaming Multiprocessor (SM) provides the following improvements over Volta and Turing. In many applications, a combination of strong and weak scaling is desirable. CUDA Toolkit is released on a monthly release cadence to deliver new features, performance improvements, and critical bug fixes. All of these products (nvidia-smi, NVML, and the NVML language bindings) are updated with each new CUDA release and provide roughly the same functionality. 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. Medium Priority: The number of threads per block should be a multiple of 32 threads, because this provides optimal computing efficiency and facilitates coalescing. 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. My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? After each change is made, ensure that the results match using whatever criteria apply to the particular algorithm. Now, if 3/4 of the running time of a sequential program is parallelized, the maximum speedup over serial code is 1 / (1 - 3/4) = 4. The example below shows how an existing example can be adapted to use the new features, guarded by the USE_CUBIN macro in this case: We recommend that the CUDA runtime be statically linked to minimize dependencies. A kernel to illustrate non-unit stride data copy. Also, because of the overhead associated with each transfer, batching many small transfers into one larger transfer performs significantly better than making each transfer separately, even if doing so requires packing non-contiguous regions of memory into a contiguous buffer and then unpacking after the transfer. For example, the NVIDIA Tesla V100 uses HBM2 (double data rate) RAM with a memory clock rate of 877 MHz and a 4096-bit-wide memory interface. It will now support actual architectures as well to emit SASS. 11.x). TF32 provides 8-bit exponent, 10-bit mantissa and 1 sign-bit. The cudaEventElapsedTime() function returns the time elapsed between the recording of the start and stop events.