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. Minimize data transfers between the host and the device. Creating additional contexts incurs memory overhead for per-context data and time overhead for context switching. A threads execution can only proceed past a __syncthreads() after all threads in its block have executed the __syncthreads(). This chapter contains a summary of the recommendations for optimization that are explained in this document. CUDA applications are built against the CUDA Runtime library, which handles device, memory, and kernel management. 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. This is an aggressive optimization that can both reduce numerical accuracy and alter special case handling. For more details refer to the memcpy_async section in the CUDA C++ Programming Guide. Last updated on Feb 27, 2023. cudaFuncAttributePreferredSharedMemoryCarveout, 1. It accepts CUDA C++ source code in character string form and creates handles that can be used to obtain the PTX. 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. 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. 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. A system with multiple GPUs may contain GPUs of different hardware versions and capabilities. Useful Features for tex1D(), tex2D(), and tex3D() Fetches, __launch_bounds__(maxThreadsPerBlock,minBlocksPerMultiprocessor), Using the CUDA Occupancy Calculator to project GPU multiprocessor occupancy, cudaOccupancyMaxActiveBlocksPerMultiprocessor, // When the program/library launches work, // When the program/library is finished with the context, Table 5. In this code, the canMapHostMemory field of the structure returned by cudaGetDeviceProperties() is used to check that the device supports mapping host memory to the devices address space. 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. Managing your GPU cluster will help achieve maximum GPU utilization and help you and your users extract the best possible performance. Note also that whenever sine and cosine of the same argument are computed, the sincos family of instructions should be used to optimize performance: __sincosf() for single-precision fast math (see next paragraph). 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. Shared memory is specified by the device architecture and is measured on per-block basis. 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. Handling New CUDA Features and Driver APIs, 15.4.1.4. Instead, all instructions are scheduled, but a per-thread condition code or predicate controls which threads execute the instructions. All CUDA threads can access it for read and write. 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. 2) In one block I need to load into shared memory the queues of other blocks. There are two options: clamp and wrap. 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. In this case the shared memory allocation size per thread block must be specified (in bytes) using an optional third execution configuration parameter, as in the following excerpt. Tuning CUDA Applications for NVIDIA Ampere GPU Architecture. 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. For slightly better performance, however, they should instead be declared as signed. Functions following the __functionName() naming convention map directly to the hardware level. Be aware that CPU-to-GPU synchronization points such as those mentioned in this section imply a stall in the GPUs processing pipeline and should thus be used sparingly to minimize their performance impact. The cudaEventElapsedTime() function returns the time elapsed between the recording of the start and stop events. As seen above, in the case of misaligned sequential accesses, caches help to alleviate the performance impact. How to notate a grace note at the start of a bar with lilypond? The CUDA Toolkits End-User License Agreement (EULA) allows for redistribution of many of the CUDA libraries under certain terms and conditions. Therefore, it is best to avoid multiple contexts per GPU within the same CUDA application. To minimize bank conflicts, it is important to understand how memory addresses map to memory banks and how to optimally schedule memory requests. The NVIDIA Management Library (NVML) is a C-based interface that provides direct access to the queries and commands exposed via nvidia-smi intended as a platform for building 3rd-party system management applications. Use several smaller thread blocks rather than one large thread block per multiprocessor if latency affects performance. CUDA kernel and thread hierarchy Starting with the Volta architecture, Independent Thread Scheduling allows a warp to remain diverged outside of the data-dependent conditional block. 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. Results obtained using double-precision arithmetic will frequently differ from the same operation performed via single-precision arithmetic due to the greater precision of the former and due to rounding issues. Shared memory is allocated per thread block, so all threads in the block have access to the same shared memory. Data Transfer Between Host and Device, 9.1.2. Hardware Acceleration for Split Arrive/Wait Barrier, 1.4.1.4. As for optimizing instruction usage, the use of arithmetic instructions that have low throughput should be avoided. Copyright 2007-2023, NVIDIA Corporation & Affiliates. The dimension and size of blocks per grid and the dimension and size of threads per block are both important factors. For more information on this pragma, refer to the CUDA C++ Programming Guide. As a result, this section discusses size but not dimension. The L2 cache set-aside size for persisting accesses may be adjusted, within limits: Mapping of user data to L2 set-aside portion can be controlled using an access policy window on a CUDA stream or CUDA graph kernel node. 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. Threads on a CPU are generally heavyweight entities. Unified Shared Memory/L1/Texture Cache, NVIDIA Ampere GPU Architecture Compatibility Guide for CUDA Applications. 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. This guide summarizes the ways that an application can be fine-tuned to gain additional speedups by leveraging the NVIDIA Ampere GPU architectures features.1. It is therefore best to redistribute the CUDA Runtime library with the application when using dynamic linking or else to statically link against the CUDA Runtime. The NVIDIA nvcc compiler driver converts .cu files into C++ for the host system and CUDA assembly or binary instructions for the device. Memory optimizations are the most important area for performance. After each round of application parallelization is complete, the developer can move to optimizing the implementation to improve performance. This Best Practices Guide is a manual to help developers obtain the best performance from NVIDIA CUDA GPUs. When using multiple GPUs from the same application, it is recommended to use GPUs of the same type, rather than mixing hardware generations. When accessing uncached local or global memory, there are hundreds of clock cycles of memory latency. Even a relatively slow kernel may be advantageous if it avoids one or more transfers between host and device memory. (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.). Accesses to the remaining data of the memory region (i.e., streaming data) are considered normal or streaming accesses and will thus use the remaining 10 MB of the non set-aside L2 portion (unless part of the L2 set-aside portion is unused). This cost has several ramifications: The complexity of operations should justify the cost of moving data to and from the device. For some architectures L1 and shared memory use same hardware and are configurable. For this workflow, a new nvptxcompiler_static library is shipped with the CUDA Toolkit. NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any time without notice. 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. Before implementing lower priority recommendations, it is good practice to make sure all higher priority recommendations that are relevant have already been applied. 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]. This utility allows administrators to query GPU device state and, with the appropriate privileges, permits administrators to modify GPU device state. 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. This is because the user could only allocate the CUDA static shared memory up to 48 KB. 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. The nature of simulating nature: A Q&A with IBM Quantum researcher Dr. Jamie We've added a "Necessary cookies only" option to the cookie consent popup. So there is no chance of memory corruption caused by overcommitting shared memory. The kernel also uses the default stream, and it will not begin execution until the memory copy completes; therefore, no explicit synchronization is needed. 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. 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. Global memory loads and stores by threads of a warp are coalesced by the device into as few as possible transactions. Page-locked or pinned memory transfers attain the highest bandwidth between the host and the device. Non-default streams are required for this overlap because memory copy, memory set functions, and kernel calls that use the default stream begin only after all preceding calls on the device (in any stream) have completed, and no operation on the device (in any stream) commences until they are finished. Theoretical bandwidth can be calculated using hardware specifications available in the product literature. An application that exhibits linear strong scaling has a speedup equal to the number of processors used. Asynchronous Copy from Global Memory to Shared Memory CUDA 11.0 introduces an async-copy feature that can be used within device code . The types of operations are an additional factor, as additions have different complexity profiles than, for example, trigonometric functions. This variant simply uses the transpose of A in place of B, so C = AAT. It can be copied into the same directory as the application executable or into a subdirectory of that installation path. This is a requirement for good performance on CUDA: the software must use a large number (generally thousands or tens of thousands) of concurrent threads. The SONAME of the library against which the application was built must match the filename of the library that is redistributed with the application. Weak Scaling and Gustafsons Law describes weak scaling, where the speedup is attained by growing the problem size.