//Set the attributes to a CUDA stream of type cudaStream_t, Mapping Persistent data accesses to set-aside L2 in sliding window experiment, /*Each CUDA thread accesses one element in the persistent data section. There are several key strategies for parallelizing sequential code. CUDA Binary (cubin) Compatibility, 15.4. To use other CUDA APIs introduced in a minor release (that require a new driver), one would have to implement fallbacks or fail gracefully. This illustrates the use of the shared memory as a user-managed cache when the hardware L1 cache eviction policy does not match up well with the needs of the application or when L1 cache is not used for reads from global memory. For GPUs with compute capability 8.6 maximum shared memory per thread block is 99 KB. The compiler can optimize groups of 4 load and store instructions. For certain devices of compute capability 5.2, L1-caching of accesses to global memory can be optionally enabled. A C-style function interface (cuda_runtime_api.h). 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 ) with sinpi(), cos(*) with cospi(), and sincos(*) with sincospi(). Devices of compute capability 1.0 to 1.3 have 16 KB/Block, compute 2.0 onwards have 48 KB/Block shared memory by default. As PTX is compiled by the CUDA driver, new toolchains will generate PTX that is not compatible with the older CUDA driver. Comparing Performance of Synchronous vs Asynchronous Copy from Global Memory to Shared Memory. 2) In one block I need to load into shared memory the queues of other blocks. On the other hand, some applications designs will require some amount of refactoring to expose their inherent parallelism. 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. Performance Improvements Optimizing C = AB Matrix Multiply
The simple remedy is to pad the shared memory array so that it has an extra column, as in the following line of code. 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. 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. Can anyone please tell me how to do these two operations? On integrated GPUs (i.e., GPUs with the integrated field of the CUDA device properties structure set to 1), mapped pinned memory is always a performance gain because it avoids superfluous copies as integrated GPU and CPU memory are physically the same. nvidia-smi is targeted at Tesla and certain Quadro GPUs, though limited support is also available on other NVIDIA GPUs. See https://developer.nvidia.com/nvidia-management-library-nvml for additional information. There is a total of 64 KB constant memory on a device. We evaluate the performance of both kernels using elements of size 4B, 8B and 16B per thread i.e., using int, int2 and int4 for the template parameter. I'm not sure if this will fit your overall processing. The results of the various optimizations are summarized in Table 2. For more information on the Runtime API, refer to CUDA Runtime of the CUDA C++ Programming Guide. This does not mean that application binaries compiled using an older toolkit will not be supported anymore. Transfers between NVLink-connected endpoints are automatically routed through NVLink, rather than PCIe. Refer to the CUDA Toolkit Release Notes for details for the minimum driver version and the version of the driver shipped with the toolkit. It supports a number of command-line parameters, of which the following are especially useful for optimization and related best practices: -maxrregcount=N specifies the maximum number of registers kernels can use at a per-file level. Resources stay allocated to each thread until it completes its execution. As with the dynamically-linked version of the CUDA Runtime library, these libraries should be bundled with the application executable when distributing that application. In this case, no warp diverges because the controlling condition is perfectly aligned with the warps. However, the SONAME of this library is given as libcublas.so.5.5: Because of this, even if -lcublas (with no version number specified) is used when linking the application, the SONAME found at link time implies that libcublas.so.5.5 is the name of the file that the dynamic loader will look for when loading the application and therefore must be the name of the file (or a symlink to the same) that is redistributed with the application. Table 2. For more details on the new Tensor Core operations refer to the Warp Matrix Multiply section in the CUDA C++ Programming Guide. Asynchronous Copy from Global Memory to Shared Memory CUDA 11.0 introduces an async-copy feature that can be used within device code . For global memory accesses, this comparison of requested memory bandwidth to actual memory bandwidth is reported by the Global Memory Load Efficiency and Global Memory Store Efficiency metrics. 1) I need to select only k blocks of out m blocks whose heads of queue is minimum k elements out of m elements. 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 throughput of __sinf(x), __cosf(x), and__expf(x) is much greater than that of sinf(x), cosf(x), and expf(x). In this case shared means that all threads in a thread block can write and read to block-allocated shared memory, and all changes to this memory will be eventually available to all threads in the block. (In Staged concurrent copy and execute, it is assumed that N is evenly divisible by nThreads*nStreams.) 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. 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. A minimum of 64 threads per block should be used, and only if there are multiple concurrent blocks per multiprocessor. For example, improving occupancy from 66 percent to 100 percent generally does not translate to a similar increase in performance. 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 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.) Checking these things frequently as an integral part of our cyclical APOD process will help ensure that we achieve the desired results as rapidly as possible. If this set-aside portion is not used by persistent accesses, then streaming or normal data accesses can use it. Floor returns the largest integer less than or equal to x. The SONAME of the library against which the application was built must match the filename of the library that is redistributed with the application. Shared Memory. Devices to be made visible to the application should be included as a comma-separated list in terms of the system-wide list of enumerable devices. 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). To use CUDA, data values must be transferred from the host to the device. Prior to UVA, an application had to keep track of which pointers referred to device memory (and for which device) and which referred to host memory as a separate bit of metadata (or as hard-coded information in the program) for each pointer. For optimal performance, users should manually tune the NUMA characteristics of their application. They produce equivalent results. 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. To scale to future devices, the number of blocks per kernel launch should be in the thousands. Two types of runtime math operations are supported. NVIDIA Ampere GPU Architecture Tuning, 1.4.1.2. 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. Thanks for contributing an answer to Stack Overflow! Shared Memory in Matrix Multiplication (C=AAT), 9.2.3.4. On devices with GDDR memory, accessing memory in a coalesced way is even more important when ECC is turned on. If you want to communicate (i.e. Distributing the CUDA Runtime and Libraries, 16.4.1. The types of operations are an additional factor, as additions have different complexity profiles than, for example, trigonometric functions. 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 other words, a cubin object generated for compute capability X.y will only execute on devices of compute capability X.z where zy. The OpenACC standard provides a set of compiler directives to specify loops and regions of code in standard C, C++ and Fortran that should be offloaded from a host CPU to an attached accelerator such as a CUDA GPU. We adjust the copy_count in the kernels such that each thread block copies from 512 bytes up to 48 MB. Asynchronous and Overlapping Transfers with Computation, 9.2.1.2. NVIDIA GPU device driver - Kernel-mode driver component for NVIDIA GPUs. Just-in-time compilation increases application load time but allows applications to benefit from latest compiler improvements. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Incorrect or unexpected results arise principally from issues of floating-point accuracy due to the way floating-point values are computed and stored. A subset of CUDA APIs dont need a new driver and they can all be used without any driver dependencies. It will not allow any other CUDA call to begin until it has completed.) The synchronous version for the kernel loads an element from global memory to an intermediate register and then stores the intermediate register value to shared memory. Upgrading dependencies is error-prone and time consuming, and in some corner cases, can even change the semantics of a program. Salient Features of Device Memory, Misaligned sequential addresses that fall within five 32-byte segments, Adjacent threads accessing memory with a stride of 2, /* Set aside max possible size of L2 cache for persisting accesses */, // Stream level attributes data structure. Armed with this knowledge, the developer can evaluate these bottlenecks for parallelization and start to investigate GPU acceleration. This advantage is increased when several powers of the same base are needed (e.g., where both x2 and x5 are calculated in close proximity), as this aids the compiler in its common sub-expression elimination (CSE) optimization. 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. 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. When statically linking to the CUDA Runtime, multiple versions of the runtime can peacably coexist in the same application process simultaneously; for example, if an application uses one version of the CUDA Runtime, and a plugin to that application is statically linked to a different version, that is perfectly acceptable, as long as the installed NVIDIA Driver is sufficient for both. This data will thus use the L2 set-aside portion. The CUDA event API provides calls that create and destroy events, record events (including a timestamp), and convert timestamp differences into a floating-point value in milliseconds. // (Must be less than cudaDeviceProp::accessPolicyMaxWindowSize), // Hint for L2 cache hit ratio for persisting accesses in the num_bytes region. 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. APIs can be deprecated and removed. Developers are notified through deprecation and documentation mechanisms of any current or upcoming changes. Then, as shown in the figure below, we specify that the accesses to the first freqSize * sizeof(int) bytes of the memory region are persistent. Floating Point Math Is not Associative, 8.2.3. Because the memory copy and the kernel both return control to the host immediately, the host function cpuFunction() overlaps their execution. Tuning the Access Window Hit-Ratio, 9.2.3.2. Choosing execution parameters is a matter of striking a balance between latency hiding (occupancy) and resource utilization. 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. The throughput of individual arithmetic operations is detailed in the CUDA C++ Programming Guide. Once the parallelism of the algorithm has been exposed, it needs to be mapped to the hardware as efficiently as possible. Many codes accomplish a significant portion of the work with a relatively small amount of code. 32/48/64/96/128K depending on the GPU and current configuration) and each block can use a chunk of it by declaring shared memory. As the host and device memories are separated, items in the host memory must occasionally be communicated between device memory and host memory as described in What Runs on a CUDA-Enabled Device?. Access to shared memory is much faster than global memory access because it is located on chip. The operating system must swap threads on and off CPU execution channels to provide multithreading capability. When redistributing the dynamically-linked versions of one or more CUDA libraries, it is important to identify the exact files that need to be redistributed. As a particular example, to evaluate the sine function in degrees instead of radians, use sinpi(x/180.0). Can airtags be tracked from an iMac desktop, with no iPhone? The CUDA driver ensures backward Binary Compatibility is maintained for compiled CUDA applications. Do new devs get fired if they can't solve a certain bug? The third generation NVLink has the same bi-directional data rate of 50 GB/s per link, but uses half the number of signal pairs to achieve this bandwidth. Does there exist a square root of Euler-Lagrange equations of a field? The NVIDIA nvcc compiler driver converts .cu files into C++ for the host system and CUDA assembly or binary instructions for 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. 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. Sequential copy and execute and Staged concurrent copy and execute demonstrate this. More details are available in the CUDA C++ Programming Guide. Its like a local cache shared among the threads of a block. Applications compiled with CUDA toolkit versions as old as 3.2 will run on newer drivers. As even CPU architectures require exposing this 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.) 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. These bindings expose the same features as the C-based interface and also provide backwards compatibility. An example would be modeling how two molecules interact with each other, where the molecule sizes are fixed. It is important to use the same divisor when calculating theoretical and effective bandwidth so that the comparison is valid. 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. Hardware Acceleration for Split Arrive/Wait Barrier, 1.4.1.4. Each floating-point arithmetic operation involves a certain amount of rounding. 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. Some calculations use 10243 instead of 109 for the final calculation. 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. For 32-bit applications, the file would be cublas32_55.dll. This is advantageous with regard to both accuracy and performance. Improvement by reading additional data into shared memory. So, when an application is built with CUDA 11.0, it can only run on a system with an R450 or later driver. (It should be mentioned that it is not possible to overlap a blocking transfer with an asynchronous transfer, because the blocking transfer occurs in the default stream, so it will not begin until all previous CUDA calls complete. If no new features are used (or if they are used conditionally with fallbacks provided) youll be able to remain compatible. 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. This is the case for: Functions operating on char or short whose operands generally need to be converted to an int, Double-precision floating-point constants (defined without any type suffix) used as input to single-precision floating-point computations. 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. The next step in optimizing memory usage is therefore to organize memory accesses according to the optimal memory access patterns. 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. A key concept in this effort is occupancy, which is explained in the following sections. 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. How to manage this resource utilization is discussed in the final sections of this chapter. Per thread resources required by a CUDA kernel might limit the maximum block size in an unwanted way. In A copy kernel that illustrates misaligned accesses, data is copied from the input array idata to the output array, both of which exist in global memory. C++-style convenience wrappers (cuda_runtime.h) built on top of the C-style functions. These results are substantially lower than the corresponding measurements for the C = AB kernel. 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. The most straightforward approach to parallelizing an application is to leverage existing libraries that take advantage of parallel architectures on our behalf. Memory Access This code reverses the data in a 64-element array using shared memory. When sharing data between threads, we need to be careful to avoid race conditions, because while threads in a block run logically in parallel, not all threads can execute physically at the same time. This technique could be used when the data dependency is such that the data can be broken into chunks and transferred in multiple stages, launching multiple kernels to operate on each chunk as it arrives. The host code in Zero-copy host code shows how zero copy is typically set up. In our experiment, we vary the size of this persistent data region from 10 MB to 60 MB to model various scenarios where data fits in or exceeds the available L2 set-aside portion of 30 MB. Not the answer you're looking for? Operations in different streams can be interleaved and in some cases overlapped - a property that can be used to hide data transfers between the host and the device. Mapped pinned host memory allows you to overlap CPU-GPU memory transfers with computation while avoiding the use of CUDA streams. 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. :class table-no-stripes, Table 3. So there is no chance of memory corruption caused by overcommitting shared memory. 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. .Z stands for the release/patch version - new updates and patches will increment this. 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. 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. rev2023.3.3.43278. A grid of N/w by M/w blocks is launched, where each thread block calculates the elements of a different tile in C from a single tile of A and a single tile of B. Block-column matrix multiplied by block-row matrix. 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. When you parallelize computations, you potentially change the order of operations and therefore the parallel results might not match sequential results. Because the driver may interleave execution of CUDA calls from other non-default streams, calls in other streams may be included in the timing. The context is explicit in the CUDA Driver API but is entirely implicit in the CUDA Runtime API, which creates and manages contexts automatically. 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. While the details of how to apply these strategies to a particular application is a complex and problem-specific topic, the general themes listed here apply regardless of whether we are parallelizing code to run on for multicore CPUs or for use on CUDA GPUs. NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any time without notice. 1 Answer Sorted by: 2 You don't need to worry about this. 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. The one exception here is when multiple threads in a warp address the same shared memory location, resulting in a broadcast. Depending on the original code, this can be as simple as calling into an existing GPU-optimized library such as cuBLAS, cuFFT, or Thrust, or it could be as simple as adding a few preprocessor directives as hints to a parallelizing compiler. 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. To understand the effect of hitRatio and num_bytes, we use a sliding window micro benchmark. However, this latency can be completely hidden by the execution of threads in other warps. In calculating each of the rows of a tile of matrix C, the entire tile of B is read. Mapping Persistent data accesses to set-aside L2 in sliding window experiment. 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. 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. Also of note is the Thrust library, which is a parallel C++ template library similar to the C++ Standard Template Library. 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. How to time code using CUDA events illustrates their use. Because it is on-chip, shared memory has much higher bandwidth and lower latency than local and global memory - provided there are no bank conflicts between the threads, as detailed in the following section. The current GPU core temperature is reported, along with fan speeds for products with active cooling. This guide introduces the Assess, Parallelize, Optimize, Deploy(APOD) design cycle for applications with the goal of helping application developers to rapidly identify the portions of their code that would most readily benefit from GPU acceleration, rapidly realize that benefit, and begin leveraging the resulting speedups in production as early as possible. An optimized handling of strided accesses using coalesced reads from global memory. For example, many kernels have complex addressing logic for accessing memory in addition to their actual computation. It allows developers to use a CUDA-enabled graphics processing unit (GPU) to accelerate processing tasks in their applications. 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. In contrast with cudaMemcpy(), the asynchronous transfer version requires pinned host memory (see Pinned Memory), and it contains an additional argument, a stream ID. 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.