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. 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. On parallel systems, it is possible to run into difficulties not typically found in traditional serial-oriented programming. This is because some operations common to each element can be performed by the thread once, amortizing the cost over the number of shared memory elements processed by the thread. To do this, the simpleMultiply kernel (Unoptimized matrix multiplication) calculates the output elements of a tile of matrix C. In Unoptimized matrix multiplication, a, b, and c are pointers to global memory for the matrices A, B, and C, respectively; blockDim.x, blockDim.y, and TILE_DIM are all equal to w. Each thread in the wxw-thread block calculates one element in a tile of C. row and col are the row and column of the element in C being calculated by a particular thread. There are two options: clamp and wrap. 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. An upgraded driver matching the CUDA runtime version is currently required for those APIs. Not the answer you're looking for? 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. 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). Best performance with synchronous copy is achieved when the copy_count parameter is a multiple of 4 for all three element sizes. So, in the previous example, had the two matrices to be added already been on the device as a result of some previous calculation, or if the results of the addition would be used in some subsequent calculation, the matrix addition should be performed locally on the device. Data should be kept on the device as long as possible. I'm not sure if this will fit your overall processing. From the performance chart, the following observations can be made for this experiment. A key aspect of correctness verification for modifications to any existing program is to establish some mechanism whereby previous known-good reference outputs from representative inputs can be compared to new results. An application has no direct control over these bank conflicts. When an application will be deployed to target machines of arbitrary/unknown configuration, the application should explicitly test for the existence of a CUDA-capable GPU in order to take appropriate action when no such device is available. Connect and share knowledge within a single location that is structured and easy to search. Since some CUDA API calls and all kernel launches are asynchronous with respect to the host code, errors may be reported to the host asynchronously as well; often this occurs the next time the host and device synchronize with each other, such as during a call to cudaMemcpy() or to cudaDeviceSynchronize(). For example, improving occupancy from 66 percent to 100 percent generally does not translate to a similar increase in performance. From supercomputers to mobile phones, modern processors increasingly rely on parallelism to provide performance. Each generation of CUDA-capable device has an associated compute capability version that indicates the feature set supported by the device (see CUDA Compute Capability). Note that the NVIDIA Tesla A100 GPU has 40 MB of total L2 cache capacity. If an appropriate native binary (cubin) is not available, but the intermediate PTX code (which targets an abstract virtual instruction set and is used for forward-compatibility) is available, then the kernel will be compiled Just In Time (JIT) (see Compiler JIT Cache Management Tools) from the PTX to the native cubin for the device. PDF L15: CUDA, cont. Memory Hierarchy and Examples Many of the industrys most popular cluster management tools support CUDA GPUs via NVML. Alternatively, NVIDIA provides an occupancy calculator in the form of an Excel spreadsheet that enables developers to hone in on the optimal balance and to test different possible scenarios more easily. Pinned memory is allocated using the cudaHostAlloc() functions in the Runtime API. 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. Furthermore, register allocations are rounded up to the nearest 256 registers per warp. CUDA driver - User-mode driver component used to run CUDA applications (e.g. The results of these calculations can frequently differ from pure 64-bit operations performed on the CUDA device. Recall that shared memory is local to each SM. For regions of system memory that have already been pre-allocated, cudaHostRegister() can be used to pin the memory on-the-fly without the need to allocate a separate buffer and copy the data into it. 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). With each generation of NVIDIA processors, new features are added to the GPU that CUDA can leverage. Registers are allocated to an entire block all at once. This value is expressed in milliseconds and has a resolution of approximately half a microsecond. The number of registers available, the maximum number of simultaneous threads resident on each multiprocessor, and the register allocation granularity vary over different compute capabilities. Both correctable single-bit and detectable double-bit errors are reported. The current board power draw and power limits are reported for products that report these measurements. // (Must be less than cudaDeviceProp::accessPolicyMaxWindowSize), // Hint for L2 cache hit ratio for persisting accesses in the num_bytes region. 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. This means that in one of these devices, for a multiprocessor to have 100% occupancy, each thread can use at most 32 registers. 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). Register pressure occurs when there are not enough registers available for a given task. 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. I think this pretty much implies that you are going to have the place the heads of each queue in global memory. See Version Management for details on how to query the available CUDA software API versions. For purposes of calculating occupancy, the number of registers used by each thread is one of the key factors. If the PTX is also not available, then the kernel launch will fail. Using shared memory to coalesce global reads. For example, if the threads of a warp access adjacent 4-byte words (e.g., adjacent float values), four coalesced 32-byte transactions will service that memory access. Applying Strong and Weak Scaling, 6.3.2. The cubins are architecture-specific. 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. On devices that are capable of concurrent copy and compute, it is possible to overlap kernel execution on the device with data transfers between the host and the device. 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. 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). Floor returns the largest integer less than or equal to x. The functions exp2(), exp2f(), exp10(), and exp10f(), on the other hand, are similar to exp() and expf() in terms of performance, and can be as much as ten times faster than their pow()/powf() equivalents. No contractual obligations are formed either directly or indirectly by this document. As PTX is compiled by the CUDA driver, new toolchains will generate PTX that is not compatible with the older CUDA driver. As described in Asynchronous and Overlapping Transfers with Computation, CUDA streams can be used to overlap kernel execution with data transfers. For the preceding procedure, assuming matrices of size NxN, there are N2 operations (additions) and 3N2 elements transferred, so the ratio of operations to elements transferred is 1:3 or O(1). The NVIDIA Ampere GPU architecture retains and extends the same CUDA programming model provided by previous NVIDIA GPU architectures such as Turing and Volta, and applications that follow the best practices for those architectures should typically see speedups on the NVIDIA A100 GPU without any code changes. The performance on a device of any compute capability can be improved by reading a tile of A into shared memory as shown in Using shared memory to improve the global memory load efficiency in matrix multiplication. The application should also maximize parallel execution at a higher level by explicitly exposing concurrent execution on the device through streams, as well as maximizing concurrent execution between the host and the device. For recent versions of CUDA hardware, misaligned data accesses are not a big issue. We can see this usage in the following example: NVRTC is a runtime compilation library for CUDA C++. A Sequential but Misaligned Access Pattern, 9.2.2.2. 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 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 third generation of NVIDIAs high-speed NVLink interconnect is implemented in A100 GPUs, which significantly enhances multi-GPU scalability, performance, and reliability with more links per GPU, much faster communication bandwidth, and improved error-detection and recovery features. Latency hiding and occupancy depend on the number of active warps per multiprocessor, which is implicitly determined by the execution parameters along with resource (register and shared memory) constraints. If you want to communicate (i.e. This is evident from the saw tooth curves. 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. Because of these nuances in register allocation and the fact that a multiprocessors shared memory is also partitioned between resident thread blocks, the exact relationship between register usage and occupancy can be difficult to determine. 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. 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. 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. Figure 6 illustrates how threads in the CUDA device can access the different memory components. The kernel also uses the default stream, and it will not begin execution until the memory copy completes; therefore, no explicit synchronization is needed. Cornell Virtual Workshop: Memory Architecture 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. 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. When you parallelize computations, you potentially change the order of operations and therefore the parallel results might not match sequential results. Consider a simple transpose of a [2048, 1024] matrix to [1024, 2048]. For example, many kernels have complex addressing logic for accessing memory in addition to their actual computation. High Priority: Minimize the use of global memory. A shared memory request for a warp is not split as with devices of compute capability 1.x, meaning that bank conflicts can occur between threads in the first half of a warp and threads in the second half of the same warp. Throughput Reported by Visual Profiler, 9.1. Note that the performance improvement is not due to improved coalescing in either case, but to avoiding redundant transfers from global memory. CUDA - shared memory - General Purpose Computing GPU - Blog The L2 cache set-aside size for persisting accesses may be adjusted, within limits: Mapping of user data to L2 set-aside portion can be controlled using an access policy window on a CUDA stream or CUDA graph kernel node. Randomly accessing. If x is the coordinate and N is the number of texels for a one-dimensional texture, then with clamp, x is replaced by 0 if x < 0 and by 1-1/N if 1 CUDA: Explainer of a kernel with 2D blocks, shared memory, atomics Answer: CUDA has different layers of memory. This chapter examines issues that can affect the correctness of returned data and points to appropriate solutions. Going a step further, if most functions are defined as __host__ __device__ rather than just __device__ functions, then these functions can be tested on both the CPU and the GPU, thereby increasing our confidence that the function is correct and that there will not be any unexpected differences in the results. Follow semantic versioning for your librarys soname. So, when an application is built with CUDA 11.0, it can only run on a system with an R450 or later driver. It provides functions to handle the following: Interoperability with OpenGL and Direct3D. An application that exhibits linear strong scaling has a speedup equal to the number of processors used. . The effective bandwidth of this kernel is 140.2 GB/s on an NVIDIA Tesla V100.These results are lower than those obtained by the final kernel for C = AB. 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. Increment major versions when there are ABI breaking changes such as API deprecation and modifications. This is advantageous with regard to both accuracy and performance. CUDA-GDB is a port of the GNU Debugger that runs on Linux and Mac; see: https://developer.nvidia.com/cuda-gdb. 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. Constant memory used for data that does not change (i.e. Setting the bank size to eight bytes can help avoid shared memory bank conflicts when accessing double precision data. 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. After this change, the effective bandwidth is 199.4 GB/s on an NVIDIA Tesla V100, which is comparable to the results from the last C = AB kernel. 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. Please note that new versions of nvidia-smi are not guaranteed to be backward-compatible with previous versions. As illustrated in Figure 7, non-unit-stride global memory accesses should be avoided whenever possible. We adjust the copy_count in the kernels such that each thread block copies from 512 bytes up to 48 MB. Bfloat16 provides 8-bit exponent i.e., same range as FP32, 7-bit mantissa and 1 sign-bit. Not requiring driver updates for new CUDA releases can mean that new versions of the software can be made available faster to users. The combined L1 cache capacity for GPUs with compute capability 8.6 is 128 KB. There are many such factors involved in selecting block size, and inevitably some experimentation is required. TO THE EXTENT NOT PROHIBITED BY LAW, IN NO EVENT WILL NVIDIA BE LIABLE FOR ANY DAMAGES, INCLUDING WITHOUT LIMITATION ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, PUNITIVE, OR CONSEQUENTIAL DAMAGES, HOWEVER CAUSED AND REGARDLESS OF THE THEORY OF LIABILITY, ARISING OUT OF ANY USE OF THIS DOCUMENT, EVEN IF NVIDIA HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. There's no way around this. Coalesced using shared memory to store a tile of A, Using shared memory to eliminate redundant reads of a tile of B. For devices with compute capability of 2.0 or greater, the Visual Profiler can be used to collect several different memory throughput measures. 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). In many applications, a combination of strong and weak scaling is desirable. For more information on the Runtime API, refer to CUDA Runtime of the CUDA C++ Programming Guide. We define binary compatibility as a set of guarantees provided by the library, where an application targeting the said library will continue to work when dynamically linked against a different version of the library. This access pattern results in four 32-byte transactions, indicated by the red rectangles. An example is transposing [1209, 9] of any type and 32 tile size. 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. A pointer to a structure with a size embedded is a better solution. It is however usually more effective to use a high-level programming language such as C++. The cudaMemcpyAsync() function is a non-blocking variant of cudaMemcpy() in which control is returned immediately to the host thread. 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. The value of this field is propagated into an application built against the library and is used to locate the library of the correct version at runtime. 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. A useful counterpart to the reference comparisons described above is to structure the code itself in such a way that is readily verifiable at the unit level. At a minimum, you would need some sort of selection process that can access the heads of each queue. FP16 / FP32 The throughput of individual arithmetic operations is detailed in the CUDA C++ Programming Guide. This is particularly beneficial to kernels that frequently call __syncthreads(). It is best to enable this option in most circumstances. 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. The CUDA Toolkit libraries (cuBLAS, cuFFT, etc.) This does not mean that application binaries compiled using an older toolkit will not be supported anymore. A C-style function interface (cuda_runtime_api.h). NVIDIA products are not designed, authorized, or warranted to be suitable for use in medical, military, aircraft, space, or life support equipment, nor in applications where failure or malfunction of the NVIDIA product can reasonably be expected to result in personal injury, death, or property or environmental damage. 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). Floating Point Math Is not Associative, 8.2.3. Recall that the initial assess step allowed the developer to determine an upper bound for the potential speedup attainable by accelerating given hotspots. 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. This also prevents array elements being repeatedly read from global memory if the same data is required several times. 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. The dimension and size of blocks per grid and the dimension and size of threads per block are both important factors. 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. It would have been more so if adjacent warps had not exhibited such a high degree of reuse of the over-fetched cache lines. The C++ host code generated by nvcc utilizes the CUDA Runtime, so applications that link to this code will depend on the CUDA Runtime; similarly, any code that uses the cuBLAS, cuFFT, and other CUDA Toolkit libraries will also depend on the CUDA Runtime, which is used internally by these libraries. For example, to use only devices 0 and 2 from the system-wide list of devices, set CUDA_VISIBLE_DEVICES=0,2 before launching the application. Effective bandwidth is calculated by timing specific program activities and by knowing how data is accessed by the program. 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. APIs can be deprecated and removed. Is it known that BQP is not contained within NP? Low Priority: Use zero-copy operations on integrated GPUs for CUDA Toolkit version 2.2 and later. High Priority: Avoid different execution paths within the same warp. 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. Another common approach to parallelization of sequential codes is to make use of parallelizing compilers. Comparing Synchronous vs Asynchronous Copy from Global Memory to Shared Memory. In this code, two streams are created and used in the data transfer and kernel executions as specified in the last arguments of the cudaMemcpyAsync call and the kernels execution configuration. These are the same contexts used implicitly by the CUDA Runtime when there is not already a current context for a thread. The size is implicitly determined from the third execution configuration parameter when the kernel is launched. A place where magic is studied and practiced? Dynamic parallelism - passing contents of shared memory to spawned blocks? Obtaining the right answer is clearly the principal goal of all computation. The SONAME of the library against which the application was built must match the filename of the library that is redistributed with the application. Between 128 and 256 threads per block is a good initial range for experimentation with different block sizes. NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any time without notice. To analyze performance, it is necessary to consider how warps access global memory in the for loop. When attempting to optimize CUDA code, it pays to know how to measure performance accurately and to understand the role that bandwidth plays in performance measurement. Sample CUDA configuration data reported by deviceQuery. These transfers are costly in terms of performance and should be minimized. A more robust approach is to selectively introduce calls to fast intrinsic functions only if merited by performance gains and where altered behavior can be tolerated. For more details refer to the L2 Access Management section in the CUDA C++ Programming Guide. While a binary compiled for 8.0 will run as is on 8.6, it is recommended to compile explicitly for 8.6 to benefit from the increased FP32 throughput. So while the impact is still evident it is not as large as we might have expected. No license, either expressed or implied, is granted under any NVIDIA patent right, copyright, or other NVIDIA intellectual property right under this document. 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 tackling other hotspots to improve the total speedup, the developer should consider taking the partially parallelized implementation and carry it through to production. 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. This is done with the FLDCW x86 assembly instruction or the equivalent operating system API. In many cases, the amount of shared memory required by a kernel is related to the block size that was chosen, but the mapping of threads to shared memory elements does not need to be one-to-one. The 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. 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.
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