These examples assume compute capability 6.0 or higher and that accesses are for 4-byte words, unless otherwise noted. To measure performance accurately, it is useful to calculate theoretical and effective bandwidth. Several third-party debuggers support CUDA debugging as well; see: https://developer.nvidia.com/debugging-solutions for more details. On devices of compute capability 2.x and 3.x, each multiprocessor has 64KB of on-chip memory that can be partitioned between L1 cache and shared memory. Asynchronous Data Copy from Global Memory to Shared Memory, 1.4.1.3. 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. Current GPUs can simultaneously process asynchronous data transfers and execute kernels. It can be copied into the same directory as the application executable or into a subdirectory of that installation path. - the incident has nothing to do with me; can I use this this way? 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. Both correctable single-bit and detectable double-bit errors are reported. CUDA kernel and thread hierarchy For more information on the Runtime API, refer to CUDA Runtime of the CUDA C++ Programming Guide. Comparing Performance of Synchronous vs Asynchronous Copy from Global Memory to Shared Memory. 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. In this section, we will review the usage patterns that may require new user workflows when taking advantage of the compatibility features of the CUDA platform. 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 results of these optimizations are summarized in Table 3. These are the same contexts used implicitly by the CUDA Runtime when there is not already a current context for a thread. Medium Priority: To hide latency arising from register dependencies, maintain sufficient numbers of active threads per multiprocessor (i.e., sufficient occupancy). PTX programs are translated at load time to the target hardware instruction set via the JIT Compiler which is part of the CUDA driver. 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 --ptxas options=v option of nvcc details the number of registers used per thread for each kernel. 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. Max and current clock rates are reported for several important clock domains, as well as the current GPU performance state (pstate). For codes continuing to make use of PTX, in order to support compiling on an older driver, your code must be first transformed into device code via the static ptxjitcompiler library or NVRTC with the option of generating code for a specific architecture (e.g. The versions of the components in the toolkit are available in this table. 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. (This was the default and only option provided in CUDA versions 5.0 and earlier.). likewise return their own sets of error codes. Data Transfer Between Host and Device provides further details, including the measurements of bandwidth between the host and the device versus within the device proper. The combined L1 cache capacity for GPUs with compute capability 8.6 is 128 KB. For devices of compute capability 8.0 (i.e., A100 GPUs) shared memory capacity per SM is 164 KB, a 71% increase compared to V100s capacity of 96 KB. As a result, Thrust can be utilized in rapid prototyping of CUDA applications, where programmer productivity matters most, as well as in production, where robustness and absolute performance are crucial. Note that the performance improvement is not due to improved coalescing in either case, but to avoiding redundant transfers from global memory. 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. This cost has several ramifications: The complexity of operations should justify the cost of moving data to and from the device. Shared memory is a powerful feature for writing well optimized CUDA code. Access to shared memory is much faster than global memory access because it is located on chip. 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. Instead, each such instruction is associated with a per-thread condition code or predicate that is set to true or false according to the controlling condition. The first and simplest case of coalescing can be achieved by any CUDA-enabled device of compute capability 6.0 or higher: the k-th thread accesses the k-th word in a 32-byte aligned array. The following examples use the cuBLAS library from CUDA Toolkit 5.5 as an illustration: In a shared library on Linux, there is a string field called the SONAME that indicates the binary compatibility level of the library. Therefore, the compiler can optimize more aggressively with signed arithmetic than it can with unsigned arithmetic. For example, on IBM Newell POWER9 nodes (where the CPUs correspond to NUMA nodes 0 and 8), use: One of the keys to good performance is to keep the multiprocessors on the device as busy as possible. For more details on the new Tensor Core operations refer to the Warp Matrix Multiply section in the CUDA C++ Programming Guide. Another way to view occupancy is the percentage of the hardwares ability to process warps that is actively in use. Exponentiation With Small Fractional Arguments, 14. On Wednesday, February 19, 2020, NVIDIA will present part 2 of a 9-part CUDA Training Series titled "CUDA Shared Memory". 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/. If you want to communicate (i.e. NVIDIA Corporation (NVIDIA) makes no representations or warranties, expressed or implied, as to the accuracy or completeness of the information contained in this document and assumes no responsibility for any errors contained herein. Production code should, however, systematically check the error code returned by each API call and check for failures in kernel launches by calling cudaGetLastError(). The hitRatio parameter can be used to specify the fraction of accesses that receive the hitProp property. In a typical system, thousands of threads are queued up for work (in warps of 32 threads each). By using new CUDA versions, users can benefit from new CUDA programming model APIs, compiler optimizations and math library features. Using the CUDA Occupancy Calculator to project GPU multiprocessor occupancy. It provides functions to handle the following: Interoperability with OpenGL and Direct3D. 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. It is recommended to use cudaMallocAsync() and cudaFreeAsync() which are stream ordered pool allocators to manage device memory. For portability, that is, to be able to execute code on future GPU architectures with higher compute capability (for which no binary code can be generated yet), an application must load PTX code that will be just-in-time compiled by the NVIDIA driver for these future devices. HBM2 memories, on the other hand, provide dedicated ECC resources, allowing overhead-free ECC protection.2. On the other hand, if the data is only accessed once, such data accesses can be considered to be streaming. This approach should be used even if one of the steps in a sequence of calculations could be performed faster on the host. 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 Unoptimized handling of strided accesses to global memory, the row-th, col-th element of C is obtained by taking the dot product of the row-th and col-th rows of A. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. However, low occupancy always interferes with the ability to hide memory latency, resulting in performance degradation. Can this be done? This feature enables CUDA kernels to overlap copying data from global to shared memory with computation. In particular, there is no register-related reason to pack data into vector data types such as float4 or int4 types. That is, a thread can safely read a memory location via texture if the location has been updated by a previous kernel call or memory copy, but not if it has been previously updated by the same thread or another thread within the same kernel call. For this workflow, a new nvptxcompiler_static library is shipped with the CUDA Toolkit. 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. This approach will tend to provide the best results for the time invested and will avoid the trap of premature optimization. These barriers can also be used alongside the asynchronous copy. All CUDA compute devices follow the IEEE 754 standard for binary floating-point representation, with some small exceptions. Replacing broken pins/legs on a DIP IC package. These instructions also avoid using extra registers for memory copies and can also bypass the L1 cache. Floating Point Math Is not Associative, 8.2.3. To get a closer match between values, set the x86 host processor to use regular double or single precision (64 bits and 32 bits, respectively). For more information on the Arrive/Wait Barriers refer to the Arrive/Wait Barrier section in the CUDA C++ Programming Guide. When using a shared or static library, follow the release notes of said library to determine if the library supports minor version compatibility. Actions that present substantial improvements for most CUDA applications have the highest priority, while small optimizations that affect only very specific situations are given a lower priority. 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.
PDF L15: CUDA, cont. Memory Hierarchy and Examples To achieve high memory bandwidth for concurrent accesses, shared memory is divided into equally sized memory modules (banks) that can be accessed simultaneously. The -use_fast_math compiler option of nvcc coerces every functionName() call to the equivalent __functionName() call. Handling New CUDA Features and Driver APIs, 15.4.1.4. 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. Upgrading dependencies is error-prone and time consuming, and in some corner cases, can even change the semantics of a program. Pinned memory is allocated using the cudaHostAlloc() functions in the Runtime API. They are faster but provide somewhat lower accuracy (e.g., __sinf(x) and __expf(x)). Occupancy is the ratio of the number of active warps per multiprocessor to the maximum number of possible active warps. To target specific versions of NVIDIA hardware and CUDA software, use the -arch, -code, and -gencode options of nvcc. Thedriver will honor the specified preference except when a kernel requires more shared memory per thread block than available in the specified configuration. Other company and product names may be trademarks of the respective companies with which they are associated. 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. Optimizations can be applied at various levels, from overlapping data transfers with computation all the way down to fine-tuning floating-point operation sequences. Furthermore, there should be multiple active blocks per multiprocessor so that blocks that arent waiting for a __syncthreads() can keep the hardware busy. These transfers are costly in terms of performance and should be minimized. Using Kolmogorov complexity to measure difficulty of problems? Hardware utilization can also be improved in some cases by designing your application so that multiple, independent kernels can execute at the same time. To use CUDA, data values must be transferred from the host to the device. Weak scaling is a measure of how the time to solution changes as more processors are added to a system with a fixed problem size per processor; i.e., where the overall problem size increases as the number of processors is increased. Pinned memory should not be overused. 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. As mentioned in the PTX section, the compilation of PTX to device code lives along with the CUDA driver, hence the generated PTX might be newer than what is supported by the driver on the deployment system. As the stride increases, the effective bandwidth decreases until the point where 32 32-byte segments are loaded for the 32 threads in a warp, as indicated in Figure 7. Table 2. The hardware splits a memory request that has bank conflicts into as many separate conflict-free requests as necessary, decreasing the effective bandwidth by a factor equal to the number of separate memory requests. When using multiple GPUs from the same application, it is recommended to use GPUs of the same type, rather than mixing hardware generations. Applications with remote random accesses may want to constrain the remotely accessed region to 64 GB for each peer GPU. I think this pretty much implies that you are going to have the place the heads of each queue in global memory. Lets assume that A and B are threads in two different warps. There's no way around this. The compiler optimizes 1.0f/sqrtf(x) into rsqrtf() only when this does not violate IEEE-754 semantics. The difference between the phonemes /p/ and /b/ in Japanese. Detecting Hardware and Software Configuration. 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. As even CPU architectures will require exposing parallelism in order to improve or simply maintain the performance of sequential applications, the CUDA family of parallel programming languages (CUDA C++, CUDA Fortran, etc.) The performance of the kernels is shown in Figure 14. Having a semantically versioned ABI means the interfaces need to be maintained and versioned. Weak Scaling and Gustafsons Law describes weak scaling, where the speedup is attained by growing the problem size. Thanks for contributing an answer to Stack Overflow! The host code in Zero-copy host code shows how zero copy is typically set up. For Windows 8, SetDefaultDLLDirectories() and AddDllDirectory() should be used instead of SetDllDirectory(). 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. No license, either expressed or implied, is granted under any NVIDIA patent right, copyright, or other NVIDIA intellectual property right under this document. When using CPU timers, it is critical to remember that many CUDA API functions are asynchronous; that is, they return control back to the calling CPU thread prior to completing their work. See Compute Capability 5.x in the CUDA C++ Programming Guide for further details. The difference is in how threads in a half warp access elements of A in the second term, a[col*TILE_DIM+i], for each iteration i. 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. The latency of most arithmetic instructions is typically 4 cycles on devices of compute capability 7.0. The NVIDIA Ampere GPU architecture allows CUDA users to control the persistence of data in L2 cache. If all threads of a warp access the same location, then constant memory can be as fast as a register access. NVIDIA shall have no liability for the consequences or use of such information or for any infringement of patents or other rights of third parties that may result from its use. Verify that your library doesnt leak dependencies, breakages, namespaces, etc. .Y stands for the minor version - Introduction of new APIs, deprecation of old APIs, and source compatibility might be broken but binary compatibility is maintained. In the code in Zero-copy host code, kernel() can reference the mapped pinned host memory using the pointer a_map in exactly the same was as it would if a_map referred to a location in device memory. Therefore, choosing sensible thread block sizes, such as multiples of the warp size (i.e., 32 on current GPUs), facilitates memory accesses by warps that are properly aligned. Functions following the __functionName() naming convention map directly to the hardware level. Whats the grammar of "For those whose stories they are"? However, striding through global memory is problematic regardless of the generation of the CUDA hardware, and would seem to be unavoidable in many cases, such as when accessing elements in a multidimensional array along the second and higher dimensions. This limitation is not specific to CUDA, but an inherent part of parallel computation on floating-point values. 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. By comparison, the smallest executable unit of parallelism on a CUDA device comprises 32 threads (termed a warp of threads). Asynchronous Copy from Global Memory to Shared Memory, 10. The reciprocal square root should always be invoked explicitly as rsqrtf() for single precision and rsqrt() for double precision. The most straightforward approach to parallelizing an application is to leverage existing libraries that take advantage of parallel architectures on our behalf. As such, the constant cache is best when threads in the same warp accesses only a few distinct locations. Shared memory enables cooperation between threads in a block. 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. 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. Its important to note that both numbers are useful. The list of active processes running on the GPU is reported, along with the corresponding process name/ID and allocated GPU memory. An explicit __syncwarp() can be used to guarantee that the warp has reconverged for subsequent instructions. Providing the two argument version of __launch_bounds__(maxThreadsPerBlock,minBlocksPerMultiprocessor) can improve performance in some cases. Thus, we can avoid the race condition described above by calling __syncthreads() after the store to shared memory and before any threads load from shared memory. The cubins are architecture-specific. The performance of the sliding-window benchmark with fixed hit-ratio of 1.0. Intermediate data structures should be created in device memory, operated on by the device, and destroyed without ever being mapped by the host or copied to host memory. 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. For GPUs with compute capability 8.6 maximum shared memory per thread block is 99 KB. NVIDIA Ampere GPU Architecture Tuning, 1.4.1.2. My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? Having understood the application profile, the developer should understand how the problem size would change if the computational performance changes and then apply either Amdahls or Gustafsons Law to determine an upper bound for the speedup. Effective bandwidth is calculated by timing specific program activities and by knowing how data is accessed by the program. The approach of using a single thread to process multiple elements of a shared memory array can be beneficial even if limits such as threads per block are not an issue. To maintain architectural compatibility, static shared memory allocations remain limited to 48 KB, and an explicit opt-in is also required to enable dynamic allocations above this limit. 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. Certain hardware features are not described by the compute capability. A portion of the L2 cache can be set aside for persistent accesses to a data region in global memory. The dimension and size of blocks per grid and the dimension and size of threads per block are both important factors. Please note that new versions of nvidia-smi are not guaranteed to be backward-compatible with previous versions. Does a summoned creature play immediately after being summoned by a ready action? See the CUDA C++ Programming Guide for further explanations and software requirements for UVA and P2P. The CUDA software environment consists of three parts: CUDA Toolkit (libraries, CUDA runtime and developer tools) - SDK for developers to build CUDA applications. On devices with GDDR memory, accessing memory in a coalesced way is even more important when ECC is turned on. You want to sort all the queues before you collect them. What is a word for the arcane equivalent of a monastery? (See Data Transfer Between Host and Device.) CUDA shared memory not faster than global? (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.)