Medium Priority: Use shared memory to avoid redundant transfers from global memory. (tens of kBs capacity) Global memory is main memory (GDDR,HBM, (1-32 GB)) and data is cached by L2,L1 caches. To do so, use this equation: \(\text{Effective\ bandwidth} = \left( {\left( B_{r} + B_{w} \right) \div 10^{9}} \right) \div \text{time}\). PTX defines a virtual machine and ISA for general purpose parallel thread execution. Shared memory can be helpful in several situations, such as helping to coalesce or eliminate redundant access to global memory. 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. Concurrent kernel execution is described below. Page-locked memory mapping is enabled by calling cudaSetDeviceFlags() with cudaDeviceMapHost. The CUDA Runtime handles kernel loading and setting up kernel parameters and launch configuration before the kernel is launched. Error counts are provided for both the current boot cycle and the lifetime of the GPU. The throughput of individual arithmetic operations is detailed in the CUDA C++ Programming Guide. It also avoids an intermediary register file access traditionally present between the global memory read and the shared memory write. 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. Low Priority: Avoid automatic conversion of doubles to floats. See Building for Maximum Compatibility for further discussion of the flags used for building code for multiple generations of CUDA-capable device simultaneously. The host runtime component of the CUDA software environment can be used only by host functions. 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. Note that no bank conflict occurs if only one memory location per bank is accessed by a half warp of threads. Using the CUDA Occupancy Calculator to project GPU multiprocessor occupancy. 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. It presents established parallelization and optimization techniques and explains coding metaphors and idioms that can greatly simplify programming for CUDA-capable GPU architectures. These examples assume compute capability 6.0 or higher and that accesses are for 4-byte words, unless otherwise noted. This number is divided by the time in seconds to obtain GB/s. \left( 0.877 \times 10^{9} \right. In some instances, operations performed by automatic NUMA balancing may degrade the performance of applications running on NVIDIA GPUs. (BFloat16 only supports FP32 as accumulator), unsigned char/signed char (8-bit precision). Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. The output for that program is shown in Figure 16. If you preorder a special airline meal (e.g. Each threadblock would do the work it needs to (e.g. (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.). This section examines the functionality, advantages, and pitfalls of both approaches. CUDA kernel and thread hierarchy 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. The dimension and size of blocks per grid and the dimension and size of threads per block are both important factors. Programmers must primarily focus on following those recommendations to achieve the best performance. Exponentiation With Small Fractional Arguments, 14. All CUDA threads can access it for read and write. (Developers targeting a single machine with known configuration may choose to skip this section.). Note that Gustafsons Law assumes that the ratio of serial to parallel execution remains constant, reflecting additional cost in setting up and handling the larger problem. 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. (For further information, refer to Performance Guidelines in the CUDA C++ Programming Guide). However, it also can act as a constraint on occupancy. :class table-no-stripes, Table 3. Any PTX device code loaded by an application at runtime is compiled further to binary code by the device driver. The __pipeline_wait_prior(0) will wait until all the instructions in the pipe object have been executed. Shared Memory and Synchronization - GPU Programming For example, the compiler may use predication to avoid an actual branch. Conversion to Warp isn't possible for Week 6-7 because there is no support for shared memory or block level synchronization. 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. One method for doing so utilizes shared memory, which is discussed in the next section. Devices of compute capability 2.0 and higher have the additional ability to multicast shared memory accesses, meaning that multiple accesses to the same location by any number of threads within a warp are served simultaneously. NVIDIA Ampere GPU Architecture Tuning, 1.4.1.2. Constantly recompiling with the latest CUDA Toolkit means forcing upgrades on the end-customers of an application product. In particular, there is no register-related reason to pack data into vector data types such as float4 or int4 types. The operating system must swap threads on and off CPU execution channels to provide multithreading capability. Concurrent copy and execute illustrates the basic technique. Is a PhD visitor considered as a visiting scholar? This capability (combined with thread synchronization) has a number of uses, such as user-managed data caches, high-performance cooperative parallel algorithms (parallel reductions, for example), and tofacilitate global memory coalescing in cases where it would otherwise not be possible. Testing of all parameters of each product is not necessarily performed by NVIDIA. When choosing the first execution configuration parameter-the number of blocks per grid, or grid size - the primary concern is keeping the entire GPU busy. Register pressure occurs when there are not enough registers available for a given task. Please see the MSDN documentation for these routines for more information. On devices of compute capability 5.x or newer, each bank has a bandwidth of 32 bits every clock cycle, and successive 32-bit words are assigned to successive banks. To learn more, see our tips on writing great answers. Any CPU timer can be used to measure the elapsed time of a CUDA call or kernel execution. Timeline comparison for copy and kernel execution. 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. Using Shared Memory in CUDA C/C++ | NVIDIA Technical Blog OpenCL is a trademark of Apple Inc. used under license to the Khronos Group Inc. NVIDIA and the NVIDIA logo are trademarks or registered trademarks of NVIDIA Corporation in the U.S. and other countries. To execute code on devices of specific compute capability, an application must load binary or PTX code that is compatible with this compute capability. An optimized handling of strided accesses using coalesced reads from global memory. The key here is that libraries are most useful when they match well with the needs of the application. Furthermore, if accesses by the threads of the warp had been permuted within or accross the four segments, still only four 32-byte transactions would have been performed by a device with compute capability 6.0 or higher. The next step in optimizing memory usage is therefore to organize memory accesses according to the optimal memory access patterns. 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. A useful technique to determine the sensitivity of performance to occupancy is through experimentation with the amount of dynamically allocated shared memory, as specified in the third parameter of the execution configuration. Each component in the toolkit is recommended to be semantically versioned. When JIT compilation of PTX device code is used, the NVIDIA driver caches the resulting binary code on disk. However, based on what you've described here, your algorithm might be amenable to an approach similar to what is outlined in the threadfence reduction sample. High Priority: Minimize data transfer between the host and the device, even if it means running some kernels on the device that do not show performance gains when compared with running them on the host CPU. This is common for building applications that are GPU architecture, platform and compiler agnostic. Because the memory copy and the kernel both return control to the host immediately, the host function cpuFunction() overlaps their execution. 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. Awareness of how instructions are executed often permits low-level optimizations that can be useful, especially in code that is run frequently (the so-called hot spot in a program). math libraries or deep learning frameworks) do not have a direct dependency on the CUDA runtime, compiler or driver. For 32-bit applications, the file would be cublas32_55.dll. Whats the grammar of "For those whose stories they are"? Now that we are working block by block, we should use shared memory. By using new CUDA versions, users can benefit from new CUDA programming model APIs, compiler optimizations and math library features. 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. How to notate a grace note at the start of a bar with lilypond? Its important to note that both numbers are useful. 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. From supercomputers to mobile phones, modern processors increasingly rely on parallelism to provide performance. However, it is possible to coalesce memory access in such cases if we use shared 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 devices of compute capability 2.0, the warp size is 32 threads and the number of banks is also 32. Instead, strategies can be applied incrementally as they are learned. For single-precision code, use of the float type and the single-precision math functions are highly recommended. 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. In short, CPU cores are designed to minimize latency for a small number of threads at a time each, whereas GPUs are designed to handle a large number of concurrent, lightweight threads in order to maximize throughput. When using branch predication, none of the instructions whose execution depends on the controlling condition is skipped. An example is transposing [1209, 9] of any type and 32 tile size. Performance Improvements Optimizing C = AB Matrix Multiply The CUDA Toolkits End-User License Agreement (EULA) allows for redistribution of many of the CUDA libraries under certain terms and conditions. For more information please refer to the section on Async Copy in the CUDA C++ Programming Guide. By understanding the end-users requirements and constraints and by applying Amdahls and Gustafsons laws, the developer can determine the upper bound of performance improvement from acceleration of the identified portions of the application. With the CUDA Driver API, a CUDA application process can potentially create more than one context for a given GPU. CUDA Compatibility Developers Guide, 15.3.1. Can this be done? As can be seen from these tables, judicious use of shared memory can dramatically improve performance. if several threads had accessed the same word or if some threads did not participate in the access), the full segment is fetched anyway. 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. 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. A portion of the L2 cache can be set aside for persistent accesses to a data region in global memory. The amount of performance benefit an application will realize by running on CUDA depends entirely on the extent to which it can be parallelized. The NVIDIA System Management Interface (nvidia-smi) is a command line utility that aids in the management and monitoring of NVIDIA GPU devices. Before addressing specific performance tuning issues covered in this guide, refer to the NVIDIA Ampere GPU Architecture Compatibility Guide for CUDA Applications to ensure that your application is compiled in a way that is compatible with the NVIDIA Ampere GPU Architecture. Choosing the execution configuration parameters should be done in tandem; however, there are certain heuristics that apply to each parameter individually. Because the size of the persistent memory region (freqSize * sizeof(int) bytes) is much, smaller than the size of the streaming memory region (dataSize * sizeof(int) bytes), data, in the persistent region is accessed more frequently*/, //Number of bytes for persisting accesses in range 10-60 MB, //Hint for cache hit ratio. A natural decomposition of the problem is to use a block and tile size of wxw threads. 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). They are faster but provide somewhat lower accuracy (e.g., __sinf(x) and __expf(x)). Coalesced using shared memory to store a tile of A, Using shared memory to eliminate redundant reads of a tile of B. NVIDIA makes no representation or warranty that products based on this document will be suitable for any specified use. 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. 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. The same goes for other CUDA Toolkit libraries: cuFFT has an interface similar to that of FFTW, etc. x86 processors can use an 80-bit double extended precision math when performing floating-point calculations. (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.). 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. 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. For devices of compute capability 8.0 (i.e., A100 GPUs) the maximum shared memory per thread block is 163 KB. Finally, this product is divided by 109 to convert the result to GB/s. The hitRatio parameter can be used to specify the fraction of accesses that receive the hitProp property. 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. Execution Configuration Optimizations, 11.1.2. These are the same contexts used implicitly by the CUDA Runtime when there is not already a current context for a thread. 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). 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. Hardware utilization can also be improved in some cases by designing your application so that multiple, independent kernels can execute at the same time. 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. See https://developer.nvidia.com/nvidia-management-library-nvml for additional information. In reality, most applications do not exhibit perfectly linear strong scaling, even if they do exhibit some degree of strong scaling. The high-priority recommendations from those guides are as follows: Find ways to parallelize sequential code. FP16 / FP32 However, compared to cache based architectures, like CPUs, latency hiding architectures, like GPUs, tend to cope better with completely random memory access patterns. Having a semantically versioned ABI means the interfaces need to be maintained and versioned. A kernel to illustrate non-unit stride data copy. 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. At a minimum, you would need some sort of selection process that can access the heads of each queue. Single-precision floats provide the best performance, and their use is highly encouraged. The maximum number of registers per thread is 255. 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. 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). For this workflow, a new nvptxcompiler_static library is shipped with the CUDA Toolkit. New APIs can be added in minor versions. GPUs with compute capability 8.6 support shared memory capacity of 0, 8, 16, 32, 64 or 100 KB per SM. Devices of compute capability 3.x have configurable bank size, which can be set using cudaDeviceSetSharedMemConfig() to either four bytes (cudaSharedMemBankSizeFourByte, thedefault) or eight bytes (cudaSharedMemBankSizeEightByte). High Priority: Avoid different execution paths within the same warp. To use CUDA, data values must be transferred from the host to the device. 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. Staging Ground Beta 1 Recap, and Reviewers needed for Beta 2, Atomic operations on Shared Memory in CUDA. So, when an application is built with CUDA 11.0, it can only run on a system with an R450 or later driver. Weak Scaling and Gustafsons Law, 3.1.3.3. CUDA Toolkit and Minimum Driver Versions. In fact, local memory is off-chip. All rights reserved. The results of these optimizations are summarized in Table 3. 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. Why do academics stay as adjuncts for years rather than move around? 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. There are two options: clamp and wrap. Mapping Persistent data accesses to set-aside L2 in sliding window experiment. Note that the performance improvement is not due to improved coalescing in either case, but to avoiding redundant transfers from global memory. 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. Warp level support for Reduction Operations, 1.4.2.1. Each warp of threads calculates one row of a tile of C, which depends on a single row of A and an entire tile of B as illustrated in Figure 12. The latency of most arithmetic instructions is typically 4 cycles on devices of compute capability 7.0. The cudaDeviceEnablePeerAccess() API call remains necessary to enable direct transfers (over either PCIe or NVLink) between GPUs. The most straightforward approach to parallelizing an application is to leverage existing libraries that take advantage of parallel architectures on our behalf. Is it known that BQP is not contained within NP? To obtain best performance in cases where the control flow depends on the thread ID, the controlling condition should be written so as to minimize the number of divergent warps. 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. Misaligned sequential addresses that fall within five 32-byte segments, Memory allocated through the CUDA Runtime API, such as via cudaMalloc(), is guaranteed to be aligned to at least 256 bytes. A system with multiple GPUs may contain GPUs of different hardware versions and capabilities. The latter case can be avoided by using single-precision floating-point constants, defined with an f suffix such as 3.141592653589793f, 1.0f, 0.5f. The available profiling tools are invaluable for guiding this process, as they can help suggest a next-best course of action for the developers optimization efforts and provide references into the relevant portions of the optimization section of this guide. 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). For example, we can write our CUDA kernels as a collection of many short __device__ functions rather than one large monolithic __global__ function; each device function can be tested independently before hooking them all together. These exceptions, which are detailed in Features and Technical Specifications of the CUDA C++ Programming Guide, can lead to results that differ from IEEE 754 values computed on the host system. All CUDA compute devices follow the IEEE 754 standard for binary floating-point representation, with some small exceptions. Current GPUs can simultaneously process asynchronous data transfers and execute kernels. There is a total of 64 KB constant memory on a device. The kernel also uses the default stream, and it will not begin execution until the memory copy completes; therefore, no explicit synchronization is needed. After each change is made, ensure that the results match using whatever criteria apply to the particular algorithm. 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. We can reuse this cache line in subsequent iterations of the loop, and we would eventually utilize all 8 words; however, when many warps execute on the same multiprocessor simultaneously, as is generally the case, the cache line may easily be evicted from the cache between iterations i and i+1. Thanks for contributing an answer to Stack Overflow! Applications already using other BLAS libraries can often quite easily switch to cuBLAS, for example, whereas applications that do little to no linear algebra will have little use for cuBLAS. One or more compute capability versions can be specified to the nvcc compiler while building a file; compiling for the native compute capability for the target GPU(s) of the application is important to ensure that application kernels achieve the best possible performance and are able to use the features that are available on a given generation of GPU. Both correctable single-bit and detectable double-bit errors are reported. vegan) just to try it, does this inconvenience the caterers and staff? CUDA Shared Memory -- Part 2 of 9 CUDA Training Series, Feb 19, 2020 Other company and product names may be trademarks of the respective companies with which they are associated. 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. Table 2. While the contents can be used as a reference manual, you should be aware that some topics are revisited in different contexts as various programming and configuration topics are explored. Answer (1 of 2): Shared memory has many more channels(and bandwidth) and works with much less latency. These results should be compared with those in Table 2. If the transfer time exceeds the execution time, a rough estimate for the overall time is tT + tE/nStreams. This chapter examines issues that can affect the correctness of returned data and points to appropriate solutions. 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. Minimize redundant accesses to global memory whenever possible. Because transfers should be minimized, programs that run multiple kernels on the same data should favor leaving the data on the device between kernel calls, rather than transferring intermediate results to the host and then sending them back to the device for subsequent calculations. Some will expect bitwise identical results, which is not always possible, especially where floating-point arithmetic is concerned; see Numerical Accuracy and Precision regarding numerical accuracy. In the C language standard, unsigned integer overflow semantics are well defined, whereas signed integer overflow causes undefined results. Lets assume that A and B are threads in two different warps. shared memory needed per block is lenP + lenS (where lenS is your blocksize + patternlength) the kernel assumes that gridDim.x * blockDim.x = lenT (the same as version 1) we can copy into shared memory in parallel (no need for for loops if you have enough threads) Share Improve this answer Follow edited Apr 15, 2011 at 19:59
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