Optimal global memory coalescing is achieved for both reads and writes because global memory is always accessed through the linear, aligned index t. The reversed index tr is only used to access shared memory, which does not have the sequential access restrictions of global memory for optimal performance. Global memory: is the memory residing graphics/accelerator card but not inside GPU chip. 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/. 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. Binary compatibility for cubins is guaranteed from one compute capability minor revision to the next one, but not from one compute capability minor revision to the previous one or across major compute capability revisions. cuda-c-best-practices-guide 12.1 documentation - NVIDIA Developer 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. The most important consideration with any profiling activity is to ensure that the workload is realistic - i.e., that information gained from the test and decisions based upon that information are relevant to real data. It also avoids an intermediary register file access traditionally present between the global memory read and the shared memory write. Distributing the CUDA Runtime and Libraries, 16.4.1. The CUDA Runtime API provides developers with high-level C++ interface for simplified management of devices, kernel executions etc., While the CUDA driver API provides (CUDA Driver API) a low-level programming interface for applications to target NVIDIA hardware. Block-column matrix (A) multiplied by block-row matrix (B) with resulting product matrix (C).. Two types of runtime math operations are supported. But this technique is still useful for other access patterns, as Ill show in the next post.). If static linking against the CUDA Runtime is impractical for some reason, then a dynamically-linked version of the CUDA Runtime library is also available. Its important to be aware that calling __syncthreads() in divergent code is undefined and can lead to deadlockall threads within a thread block must call __syncthreads() at the same point. 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. Memory Access The NVIDIA Ampere GPU architecture allows CUDA users to control the persistence of data in L2 cache. For those exponentiations where the exponent is not exactly representable as a floating-point number, such as 1/3, this can also provide much more accurate results, as use of pow() magnifies the initial representational error. The way to avoid strided access is to use shared memory as before, except in this case a warp reads a row of A into a column of a shared memory tile, as shown in An optimized handling of strided accesses using coalesced reads from global memory. Delays in rolling out new NVIDIA drivers could mean that users of such systems may not have access to new features available in CUDA releases. 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. For example, many kernels have complex addressing logic for accessing memory in addition to their actual computation. Figure 6 illustrates such a situation; in this case, threads within a warp access words in memory with a stride of 2. However, if multiple addresses of a memory request map to the same memory bank, the accesses are serialized. This makes the code run faster at the cost of diminished precision and accuracy. This situation is not different from what is available today where developers use macros to compile out features based on CUDA versions. CUDA Shared Memory - Oak Ridge Leadership Computing Facility Memory instructions include any instruction that reads from or writes to shared, local, or global memory. Thrust provides a rich collection of data parallel primitives such as scan, sort, and reduce, which can be composed together to implement complex algorithms with concise, readable source code. 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. By default, the nvcc compiler generates IEEE-compliant code, but it also provides options to generate code that somewhat less accurate but faster: -ftz=true (denormalized numbers are flushed to zero), -prec-sqrt=false (less precise square root). The cudaMemcpyAsync() function is a non-blocking variant of cudaMemcpy() in which control is returned immediately to the host thread. Declare shared memory in CUDA C/C++ device code using the__shared__variable declaration specifier. Having identified the hotspots and having done the basic exercises to set goals and expectations, the developer needs to parallelize the code. Strong Scaling and Amdahls Law, 3.1.3.2. Is it known that BQP is not contained within NP? A stream is simply a sequence of operations that are performed in order on the device. With UVA, the host memory and the device memories of all installed supported devices share a single virtual address space. Finally, higher bandwidth between the host and the device is achieved when using page-locked (or pinned) memory, as discussed in the CUDA C++ Programming Guide and the Pinned Memory section of this document. Copyright 2020-2023, NVIDIA Corporation & Affiliates. Sometimes, the best optimization might even be to avoid any data transfer in the first place by simply recomputing the data whenever it is needed. 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 more information please refer to the section on Async Copy in the CUDA C++ Programming Guide. Shared memory enables cooperation between threads in a block. To keep the kernels simple, M and N are multiples of 32, since the warp size (w) is 32 for current devices. When our CUDA 11.1 application (i.e. 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. However, the set of registers (known as the register file) is a limited commodity that all threads resident on a multiprocessor must share. Low Priority: Make it easy for the compiler to use branch predication in lieu of loops or control statements. The NVML API is shipped with the CUDA Toolkit (since version 8.0) and is also available standalone on the NVIDIA developer website as part of the GPU Deployment Kit through a single header file accompanied by PDF documentation, stub libraries, and sample applications; see https://developer.nvidia.com/gpu-deployment-kit. It is customers sole responsibility to evaluate and determine the applicability of any information contained in this document, ensure the product is suitable and fit for the application planned by customer, and perform the necessary testing for the application in order to avoid a default of the application or the product. This variant simply uses the transpose of A in place of B, so C = AAT. A CUDA device has a number of different memory components that are available to programmers - register, shared memory, local memory, global memory and constant memory. (See Data Transfer Between Host and Device.) These accept one of three options:cudaFuncCachePreferNone, cudaFuncCachePreferShared, and cudaFuncCachePreferL1. Often this means the use of directives-based approaches, where the programmer uses a pragma or other similar notation to provide hints to the compiler about where parallelism can be found without needing to modify or adapt the underlying code itself. It would have been more so if adjacent warps had not exhibited such a high degree of reuse of the over-fetched cache lines. A minimum of 64 threads per block should be used, and only if there are multiple concurrent blocks per multiprocessor. No license, either expressed or implied, is granted under any NVIDIA patent right, copyright, or other NVIDIA intellectual property right under this document. Ensure global memory accesses are coalesced. 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. The throughput of individual arithmetic operations is detailed in the CUDA C++ Programming Guide. This feature enables CUDA kernels to overlap copying data from global to shared memory with computation. Threads on a CPU are generally heavyweight entities. Shared Memory in Matrix Multiplication (C=AB), 9.2.3.3. 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. CUDA: Shared memory allocation with overlapping borders For small integer powers (e.g., x2 or x3), explicit multiplication is almost certainly faster than the use of general exponentiation routines such as pow(). 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. This ensures your code is compatible. Asynchronous copy achieves better performance in nearly all cases. On the other hand, if the data is only accessed once, such data accesses can be considered to be streaming. This chapter discusses the various kinds of memory on the host and device and how best to set up data items to use the memory effectively. A stride of 2 results in a 50% of load/store efficiency since half the elements in the transaction are not used and represent wasted bandwidth. Details about occupancy are displayed in the Occupancy section. However, it also can act as a constraint on occupancy. Shared memory is extremely fast, user managed, on-chip memory that can be used to share data between threads within a thread block. Load the GPU program and execute, caching data on-chip for performance. 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). Last updated on Feb 27, 2023. cudaFuncAttributePreferredSharedMemoryCarveout, 1. This is important for a number of reasons; for example, it allows the user to profit from their investment as early as possible (the speedup may be partial but is still valuable), and it minimizes risk for the developer and the user by providing an evolutionary rather than revolutionary set of changes to the application. The warp wide reduction operations support arithmetic add, min, and max operations on 32-bit signed and unsigned integers and bitwise and, or and xor operations on 32-bit unsigned integers. 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. 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. When accessing uncached local or global memory, there are hundreds of clock cycles of memory latency. 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. Choosing the execution configuration parameters should be done in tandem; however, there are certain heuristics that apply to each parameter individually. For this reason, ensuring that as much as possible of the data in each cache line fetched is actually used is an important part of performance optimization of memory accesses on these devices. Kernel access to global memory also should be minimized by maximizing the use of shared memory on the device. BFloat16 format is especially effective for DL training scenarios. Why do academics stay as adjuncts for years rather than move around? By exposing parallelism to the compiler, directives allow the compiler to do the detailed work of mapping the computation onto the parallel architecture. Before we proceed further on this topic, its important for developers to understand the concept of Minimum Driver Version and how that may affect them. With the CUDA Driver API, a CUDA application process can potentially create more than one context for a given GPU. How to time code using CUDA events illustrates their use. 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. The one exception here is when multiple threads in a warp address the same shared memory location, resulting in a broadcast. Threads copy the data from global memory to shared memory with the statement s[t] = d[t], and the reversal is done two lines later with the statement d[t] = s[tr]. Managing your GPU cluster will help achieve maximum GPU utilization and help you and your users extract the best possible performance. Its result will often differ slightly from results obtained by doing the two operations separately. 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 latency of most arithmetic instructions is typically 4 cycles on devices of compute capability 7.0. 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. Both correctable single-bit and detectable double-bit errors are reported. Testing of all parameters of each product is not necessarily performed by NVIDIA. 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. Another, more aggressive, option is -use_fast_math, which coerces every functionName() call to the equivalent __functionName() call. All kernel launches are asynchronous, as are memory-copy functions with the Async suffix on their names. So, if each thread block uses many registers, the number of thread blocks that can be resident on a multiprocessor is reduced, thereby lowering the occupancy of the multiprocessor. In such cases, kernels with 32x32 or 64x16 threads can be launched with each thread processing four elements of the shared memory array. 2) In one block I need to load into shared memory the queues of other blocks. 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. cudaDeviceSynchronize()blocks the calling CPU thread until all CUDA calls previously issued by the thread are completed. CUDA Binary (cubin) Compatibility, 15.4. We can see this usage in the following example: NVRTC is a runtime compilation library for CUDA C++. 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. One way to use shared memory that leverages such thread cooperation is to enable global memory coalescing, as demonstrated by the array reversal in this post. This is done by carefully choosing the execution configuration of each kernel launch. As illustrated in Figure 7, non-unit-stride global memory accesses should be avoided whenever possible. An example would be modeling how two molecules interact with each other, where the molecule sizes are fixed. 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. See Version Management for details on how to query the available CUDA software API versions. 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 an existing project, the first step is to assess the application to locate the parts of the code that are responsible for the bulk of the execution time. 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. NVIDIA hereby expressly objects to applying any customer general terms and conditions with regards to the purchase of the NVIDIA product referenced in this document. In the NVIDIA Ampere GPU architecture remote NVLINK accesses go through a Link TLB on the remote GPU. On devices with GDDR memory, accessing memory in a coalesced way is even more important when ECC is turned on. Of these different memory spaces, global memory is the most plentiful; see Features and Technical Specifications of the CUDA C++ Programming Guide for the amounts of memory available in each memory space at each compute capability level. In some instances, operations performed by automatic NUMA balancing may degrade the performance of applications running on NVIDIA GPUs. It provides functions to handle the following: Interoperability with OpenGL and Direct3D. 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. Whether a device has this capability is indicated by the asyncEngineCount field of the cudaDeviceProp structure (or listed in the output of the deviceQuery CUDA Sample). 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. Not all threads need to participate. This allows applications that depend on these libraries to redistribute the exact versions of the libraries against which they were built and tested, thereby avoiding any trouble for end users who might have a different version of the CUDA Toolkit (or perhaps none at all) installed on their machines. It accepts CUDA C++ source code in character string form and creates handles that can be used to obtain the PTX. Zero copy can be used in place of streams because kernel-originated data transfers automatically overlap kernel execution without the overhead of setting up and determining the optimal number of streams. The access requirements for coalescing depend on the compute capability of the device and are documented in the CUDA C++ Programming Guide. 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). If this happens, the different execution paths must be executed separately; this increases the total number of instructions executed for this warp. 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. This capability makes them well suited to computations that can leverage parallel execution. The remaining portion of this persistent data will be accessed using the streaming property. 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. The details of various CPU timing approaches are outside the scope of this document, but developers should always be aware of the resolution their timing calls provide. For single-precision code, use of the float type and the single-precision math functions are highly recommended. Using shared memory to improve the global memory load efficiency in matrix multiplication. In the previous post, I looked at how global memory accesses by a group of threads can be coalesced into a single transaction, and howalignment and stride affect coalescing for various generations of CUDA hardware. However we now add the underlying driver to that mix. 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. As seen above, in the case of misaligned sequential accesses, caches help to alleviate the performance impact. 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. This kernel has an effective bandwidth of 144.4 GB/s on an NVIDIA Tesla V100. For GPUs with compute capability 8.6, shared memory capacity per SM is 100 KB. NVRTC used to support only virtual architectures through the option -arch, since it was only emitting PTX. CUDA 11.0 introduces an async-copy feature that can be used within device code to explicitly manage the asynchronous copying of data from global memory to shared memory. Kernels can be written using the CUDA instruction set architecture, called PTX, which is described in the PTX reference manual. However, if multiple threads requested addresses map to the same memory bank, the accesses are serialized. Sharing data between blocks - CUDA Programming and Performance - NVIDIA As an example, the assignment operator in the following sample code has a high throughput, but, crucially, there is a latency of hundreds of clock cycles to read data from global memory: Much of this global memory latency can be hidden by the thread scheduler if there are sufficient independent arithmetic instructions that can be issued while waiting for the global memory access to complete. A further improvement can be made to how Using shared memory to improve the global memory load efficiency in matrix multiplication deals with matrix B. Always check the error return values on all CUDA API functions, even for functions that are not expected to fail, as this will allow the application to detect and recover from errors as soon as possible should they occur. These results are substantially lower than the corresponding measurements for the C = AB kernel. What is a word for the arcane equivalent of a monastery? This also prevents array elements being repeatedly read from global memory if the same data is required several times. The results of these optimizations are summarized in Table 3. 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. 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. Using unrealistic workloads can lead to sub-optimal results and wasted effort both by causing developers to optimize for unrealistic problem sizes and by causing developers to concentrate on the wrong functions. 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. Consequently, the order in which arithmetic operations are performed is important. Optimizations can be applied at various levels, from overlapping data transfers with computation all the way down to fine-tuning floating-point operation sequences. For optimal performance, users should manually tune the NUMA characteristics of their application. Starting with the Volta architecture, Independent Thread Scheduling allows a warp to remain diverged outside of the data-dependent conditional block. The next step in optimizing memory usage is therefore to organize memory accesses according to the optimal memory access patterns. 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. To analyze performance, it is necessary to consider how warps access global memory in the for loop. If such an application is run on a system with the R418 driver installed, CUDA initialization will return an error as can be seen in the example below.
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