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). 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. Current utilization rates are reported for both the compute resources of the GPU and the memory interface. 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. The next step in optimizing memory usage is therefore to organize memory accesses according to the optimal memory access patterns. Some will expect bitwise identical results, which is not always possible, especially where floating-point arithmetic is concerned; see Numerical Accuracy and Precision regarding numerical accuracy. Adjust kernel launch configuration to maximize device utilization. Coalesced using shared memory to store a tile of A, Using shared memory to eliminate redundant reads of a tile of B. If it has not, subsequent compilation phases might still decide otherwise, if they find the variable consumes too much register space for the targeted architecture. Using asynchronous copies does not use any intermediate register. Now I have some problems. Note that no bank conflict occurs if only one memory location per bank is accessed by a half warp of threads. As with the dynamically-linked version of the CUDA Runtime library, these libraries should be bundled with the application executable when distributing that application. 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. An application has no direct control over these bank conflicts. The results of these optimizations are summarized in Table 3. nvidia-smi is targeted at Tesla and certain Quadro GPUs, though limited support is also available on other NVIDIA GPUs. Shared memory Bank Conflicts: Shared memory bank conflicts exist and are common for the strategy used. exchange data) between threadblocks, the only method is to use global memory. For regions of system memory that have already been pre-allocated, cudaHostRegister() can be used to pin the memory on-the-fly without the need to allocate a separate buffer and copy the data into it. Medium Priority: Use the fast math library whenever speed trumps precision. 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. aims to make the expression of this parallelism as simple as possible, while simultaneously enabling operation on CUDA-capable GPUs designed for maximum parallel throughput. While the contents can be used as a reference manual, you should be aware that some topics are revisited in different contexts as various programming and configuration topics are explored. 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. For devices of compute capability 2.0, the warp size is 32 threads and the number of banks is also 32. As can be seen from these tables, judicious use of shared memory can dramatically improve performance. The high-priority recommendations from those guides are as follows: Find ways to parallelize sequential code. To specify an alternate path where the libraries will be distributed, use linker options similar to those below: For Linux and Mac, the -rpath option is used as before. 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. It can be simpler to view N as a very large number, which essentially transforms the equation into \(S = 1/(1 - P)\). The throughput of individual arithmetic operations is detailed in the CUDA C++ Programming Guide. Asynchronous Data Copy from Global Memory to Shared Memory, 1.4.1.3. For more details on the new warp wide reduction operations refer to Warp Reduce Functions in the CUDA C++ Programming Guide. These are the primary hardware differences between CPU hosts and GPU devices with respect to parallel programming. 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. Applications compiled with CUDA toolkit versions as old as 3.2 will run on newer drivers. So there is no chance of memory corruption caused by overcommitting shared memory. After each round of application parallelization is complete, the developer can move to optimizing the implementation to improve performance. It is however usually more effective to use a high-level programming language such as C++. Increment major versions when there are ABI breaking changes such as API deprecation and modifications. This recommendation is subject to resource availability; therefore, it should be determined in the context of the second execution parameter - the number of threads per block, or block size - as well as shared memory usage. Shared memory is a powerful feature for writing well-optimized CUDA code. On devices with GDDR memory, accessing memory in a coalesced way is even more important when ECC is turned on. Page-locked or pinned memory transfers attain the highest bandwidth between the host and the device. Staged concurrent copy and execute shows how the transfer and kernel execution can be broken up into nStreams stages. For example, Overlapping computation and data transfers demonstrates how host computation in the routine cpuFunction() is performed while data is transferred to the device and a kernel using the device is executed. Aside from memory bank conflicts, there is no penalty for non-sequential or unaligned accesses by a warp in shared memory. Copyright 2007-2023, NVIDIA Corporation & Affiliates. For example, if the install name of the cuBLAS library is given as @rpath/libcublas.5.5.dylib, then the library is version 5.5 and the copy of this library redistributed with the application must be named libcublas.5.5.dylib, even though only -lcublas (with no version number specified) is used at link time. The CUDA software environment consists of three parts: CUDA Toolkit (libraries, CUDA runtime and developer tools) - SDK for developers to build CUDA applications. In such cases, kernels with 32x32 or 64x16 threads can be launched with each thread processing four elements of the shared memory array. Note that the NVIDIA Tesla A100 GPU has 40 MB of total L2 cache capacity. With each generation of NVIDIA processors, new features are added to the GPU that CUDA can leverage. Code samples throughout the guide omit error checking for conciseness. The examples in this section have illustrated three reasons to use shared memory: To enable coalesced accesses to global memory, especially to avoid large strides (for general matrices, strides are much larger than 32), To eliminate (or reduce) redundant loads from global memory. Reproduction of information in this document is permissible only if approved in advance by NVIDIA in writing, reproduced without alteration and in full compliance with all applicable export laws and regulations, and accompanied by all associated conditions, limitations, and notices. On devices of compute capability 5.x or newer, each bank has a bandwidth of 32 bits every clock cycle, and successive 32-bit words are assigned to successive banks. 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. 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. Minimize data transfers between the host and the device. The interface is augmented to retrieve either the PTX or cubin if an actual architecture is specified. TF32 provides 8-bit exponent, 10-bit mantissa and 1 sign-bit. It is faster than global 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. The results of the various optimizations are summarized in Table 2. To use dynamic linking with the CUDA Runtime when using the nvcc from CUDA 5.5 or later to link the application, add the --cudart=shared flag to the link command line; otherwise the statically-linked CUDA Runtime library is used by default. Threads on a CPU are generally heavyweight entities. 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. Weaknesses in customers product designs may affect the quality and reliability of the NVIDIA product and may result in additional or different conditions and/or requirements beyond those contained in this document. The -use_fast_math compiler option of nvcc coerces every functionName() call to the equivalent __functionName() call. 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). When an application will be deployed to target machines of arbitrary/unknown configuration, the application should explicitly test for the existence of a CUDA-capable GPU in order to take appropriate action when no such device is available. Not the answer you're looking for? By describing your computation in terms of these high-level abstractions you provide Thrust with the freedom to select the most efficient implementation automatically. Shared memory is extremely fast, user managed, on-chip memory that can be used to share data between threads within a thread block. On devices that are capable of concurrent copy and compute, it is possible to overlap kernel execution on the device with data transfers between the host and the device. For more information on this pragma, refer to the CUDA C++ Programming Guide. This is called just-in-time compilation (JIT). This cost has several ramifications: The complexity of operations should justify the cost of moving data to and from the device. Prior to CUDA 11.0, the minimum driver version for a toolkit was the same as the driver shipped with that version of the CUDA Toolkit. Low Priority: Use shift operations to avoid expensive division and modulo calculations. The compiler must on occasion insert conversion instructions, introducing additional execution cycles. By understanding how applications can scale it is possible to set expectations and plan an incremental parallelization strategy. These barriers can also be used alongside the asynchronous copy. We can see this usage in the following example: NVRTC is a runtime compilation library for CUDA C++. CUDA Toolkit Library Redistribution, 16.4.1.2. If you want to communicate (i.e. Before addressing specific performance tuning issues covered in this guide, refer to the NVIDIA Ampere GPU Architecture Compatibility Guide for CUDA Applications to ensure that your application is compiled in a way that is compatible with the NVIDIA Ampere GPU Architecture. New APIs can be added in minor versions. In many cases, the amount of shared memory required by a kernel is related to the block size that was chosen, but the mapping of threads to shared memory elements does not need to be one-to-one. In short, CPU cores are designed to minimize latency for a small number of threads at a time each, whereas GPUs are designed to handle a large number of concurrent, lightweight threads in order to maximize throughput. Shared memory can be thought of as a software-controlled cache on the processor - each Streaming Multiprocessor has a small amount of shared memory (e.g. These instructions also avoid using extra registers for memory copies and can also bypass the L1 cache. As a result, all modern processors require parallel code in order to achieve good utilization of their computational power. 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. In Using shared memory to improve the global memory load efficiency in matrix multiplication, each element in a tile of A is read from global memory only once, in a fully coalesced fashion (with no wasted bandwidth), to shared memory. This section examines the functionality, advantages, and pitfalls of both approaches. Armed with this knowledge, the developer can evaluate these bottlenecks for parallelization and start to investigate GPU acceleration. However, it is best to avoid accessing global memory whenever possible. 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. 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. The cubins are architecture-specific. Information published by NVIDIA regarding third-party products or services does not constitute a license from NVIDIA to use such products or services or a warranty or endorsement thereof. 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. Each threadblock would do the work it needs to (e.g. Data Transfer Between Host and Device, 9.1.2. The performance of the sliding-window benchmark with tuned hit-ratio. But this technique is still useful for other access patterns, as Ill show in the next post.). For example, transferring two matrices to the device to perform a matrix addition and then transferring the results back to the host will not realize much performance benefit. 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. Applications with remote random accesses may want to constrain the remotely accessed region to 64 GB for each peer GPU. It is worth noting that several of the other functions in the above example also take up a significant portion of the overall running time, such as calcStats() and calcSummaryData(). See Hardware Multithreading of the CUDA C++ Programming Guide for the register allocation formulas for devices of various compute capabilities and Features and Technical Specifications of the CUDA C++ Programming Guide for the total number of registers available on those devices. Zero copy is a feature that was added in version 2.2 of the CUDA Toolkit. Do new devs get fired if they can't solve a certain bug? 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. Prefer shared memory access where possible. 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. \times (4096/8) \times 2 \right) \div 10^{9} = 898\text{GB/s}\). Because of these nuances in register allocation and the fact that a multiprocessors shared memory is also partitioned between resident thread blocks, the exact relationship between register usage and occupancy can be difficult to determine. Shared memory is magnitudes faster to access than global memory. Your code might reflect different priority factors. Examples include modeling fluids or structures as meshes or grids and some Monte Carlo simulations, where increasing the problem size provides increased accuracy.