Shared memory enables cooperation between threads in a block. 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. (See Data Transfer Between Host and Device.) As with the previous section on library building recommendations, if using the CUDA runtime, we recommend linking to the CUDA runtime statically when building your application. 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. 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. 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. This difference is illustrated in Figure 13. If A, B, and C are floating-point values, (A+B)+C is not guaranteed to equal A+(B+C) as it is in symbolic math. Package managers facilitate this process but unexpected issues can still arise and if a bug is found, it necessitates a repeat of the above upgrade process. Global memory loads and stores by threads of a warp are coalesced by the device into as few as possible transactions. 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. In order to optimize the performance, when the size of the persistent data is more than the size of the set-aside L2 cache portion, we tune the num_bytes and hitRatio parameters in the access window as below. Connect and share knowledge within a single location that is structured and easy to search. However, a few rules of thumb should be followed: Threads per block should be a multiple of warp size to avoid wasting computation on under-populated warps and to facilitate coalescing. Such a pattern is shown in Figure 3. Built on top of these technologies are CUDA libraries, some of which are included in the CUDA Toolkit, while others such as cuDNN may be released independently of the CUDA Toolkit. CUDA Binary (cubin) Compatibility, 15.4. (Note that the CUDA compiler considers any device code that does not contribute to a write to global memory as dead code subject to elimination, so we must at least write something out to global memory as a result of our addressing logic in order to successfully apply this strategy.). 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). It will not allow any other CUDA call to begin until it has completed.) Before tackling other hotspots to improve the total speedup, the developer should consider taking the partially parallelized implementation and carry it through to production. Even though such an access requires only 1 transaction on devices of compute capability 2.0 or higher, there is wasted bandwidth in the transaction, because only one 4-byte word out of 8 words in a 32-byte cache segment is used. CUDA-GDB is a port of the GNU Debugger that runs on Linux and Mac; see: https://developer.nvidia.com/cuda-gdb. 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. The actual memory throughput shows how close the code is to the hardware limit, and a comparison of the effective or requested bandwidth to the actual bandwidth presents a good estimate of how much bandwidth is wasted by suboptimal coalescing of memory accesses (see Coalesced Access to Global Memory). The CUDA driver ensures backward Binary Compatibility is maintained for compiled CUDA applications. 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. There's no way around this. if several threads had accessed the same word or if some threads did not participate in the access), the full segment is fetched anyway. With the use of shared memory we can fetch data from global memory and place it into on-chip memory with far lower latency and higher bandwidth then global memory. 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. The optimal NUMA tuning will depend on the characteristics and desired hardware affinities of each application and node, but in general applications computing on NVIDIA GPUs are advised to choose a policy that disables automatic NUMA balancing. 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. 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. 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. The only performance issue with shared memory is bank conflicts, which we will discuss later. The async-copy does not require the copy_count parameter to be a multiple of 4, to maximize performance through compiler optimizations. The dynamic shared memory kernel, dynamicReverse(), declares the shared memory array using an unsized extern array syntax, extern shared int s[] (note the empty brackets and use of the extern specifier). It accepts CUDA C++ source code in character string form and creates handles that can be used to obtain the PTX. There are many possible approaches to profiling the code, but in all cases the objective is the same: to identify the function or functions in which the application is spending most of its execution time. For example, if the hitRatio value is 0.6, 60% of the memory accesses in the global memory region [ptr..ptr+num_bytes) have the persisting property and 40% of the memory accesses have the streaming property. Both pow() and powf() are heavy-weight functions in terms of register pressure and instruction count due to the numerous special cases arising in general exponentiation and the difficulty of achieving good accuracy across the entire ranges of the base and the exponent. It enables GPU threads to directly access host memory. :class table-no-stripes, Table 3. For more information please refer to the section on Async Copy in the CUDA C++ Programming Guide. 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. 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. These accept one of three options:cudaFuncCachePreferNone, cudaFuncCachePreferShared, and cudaFuncCachePreferL1. 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. Modern NVIDIA GPUs can support up to 2048 active threads concurrently per multiprocessor (see Features and Specifications of the CUDA C++ Programming Guide) On GPUs with 80 multiprocessors, this leads to more than 160,000 concurrently active threads. For the preceding procedure, assuming matrices of size NxN, there are N2 operations (additions) and 3N2 elements transferred, so the ratio of operations to elements transferred is 1:3 or O(1). The CUDA Toolkit includes a number of such libraries that have been fine-tuned for NVIDIA CUDA GPUs, such as cuBLAS, cuFFT, and so on. As a result, all modern processors require parallel code in order to achieve good utilization of their computational power. 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. Functions following functionName() naming convention are slower but have higher accuracy (e.g., sinf(x) and expf(x)). Answer (1 of 2): Shared memory has many more channels(and bandwidth) and works with much less latency. Each generation of CUDA-capable device has an associated compute capability version that indicates the feature set supported by the device (see CUDA Compute Capability). THIS DOCUMENT AND ALL NVIDIA DESIGN SPECIFICATIONS, REFERENCE BOARDS, FILES, DRAWINGS, DIAGNOSTICS, LISTS, AND OTHER DOCUMENTS (TOGETHER AND SEPARATELY, MATERIALS) ARE BEING PROVIDED AS IS. NVIDIA MAKES NO WARRANTIES, EXPRESSED, IMPLIED, STATUTORY, OR OTHERWISE WITH RESPECT TO THE MATERIALS, AND EXPRESSLY DISCLAIMS ALL IMPLIED WARRANTIES OF NONINFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE. On Linux systems, the CUDA driver and kernel mode components are delivered together in the NVIDIA display driver package. The compiler replaces a branch instruction with predicated instructions only if the number of instructions controlled by the branch condition is less than or equal to a certain threshold. Another, more aggressive, option is -use_fast_math, which coerces every functionName() call to the equivalent __functionName() call. Using UVA, on the other hand, the physical memory space to which a pointer points can be determined simply by inspecting the value of the pointer using cudaPointerGetAttributes(). In these cases, no warp can ever diverge. 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. Texture references that are bound to CUDA arrays can be written to via surface-write operations by binding a surface to the same underlying CUDA array storage). Other differences are discussed as they arise elsewhere in this document. Kernel access to global memory also should be minimized by maximizing the use of shared memory on the device. 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. Not requiring driver updates for new CUDA releases can mean that new versions of the software can be made available faster to users. 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 is evident from the saw tooth curves. However, this latency can be completely hidden by the execution of threads in other warps. Armed with this knowledge, the developer can evaluate these bottlenecks for parallelization and start to investigate GPU acceleration. Global memory: is the memory residing graphics/accelerator card but not inside GPU chip. APOD is a cyclical process: initial speedups can be achieved, tested, and deployed with only minimal initial investment of time, at which point the cycle can begin again by identifying further optimization opportunities, seeing additional speedups, and then deploying the even faster versions of the application into production. 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. If this set-aside portion is not used by persistent accesses, then streaming or normal data accesses can use it. Managing your GPU cluster will help achieve maximum GPU utilization and help you and your users extract the best possible performance. (This was the default and only option provided in CUDA versions 5.0 and earlier.). How to notate a grace note at the start of a bar with lilypond? Block-column matrix (A) multiplied by block-row matrix (B) with resulting product matrix (C).. Integer division and modulo operations are particularly costly and should be avoided or replaced with bitwise operations whenever possible: If \(n\) is a power of 2, ( \(i/n\) ) is equivalent to ( \(i \gg {log2}(n)\) ) and ( \(i\% n\) ) is equivalent to ( \(i\&\left( {n - 1} \right)\) ). On Windows, if the CUDA Runtime or other dynamically-linked CUDA Toolkit library is placed in the same directory as the executable, Windows will locate it automatically. There are two options: clamp and wrap. To obtain best performance in cases where the control flow depends on the thread ID, the controlling condition should be written so as to minimize the number of divergent warps. Support for bitwise AND along with bitwise XOR which was introduced in Turing, through BMMA instructions. To verify the exact DLL filename that the application expects to find at runtime, use the dumpbin tool from the Visual Studio command prompt: Once the correct library files are identified for redistribution, they must be configured for installation into a location where the application will be able to find them. Starting with the Volta architecture, Independent Thread Scheduling allows a warp to remain diverged outside of the data-dependent conditional block. 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. 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. (BFloat16 only supports FP32 as accumulator), unsigned char/signed char (8-bit precision). In a typical system, thousands of threads are queued up for work (in warps of 32 threads each). Each component in the toolkit is recommended to be semantically versioned. This approach will greatly improve your understanding of effective programming practices and enable you to better use the guide for reference later. As seen above, in the case of misaligned sequential accesses, caches help to alleviate the performance impact. Hardware utilization can also be improved in some cases by designing your application so that multiple, independent kernels can execute at the same time. // Type of access property on cache miss. 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. Within each iteration of the for loop, a value in shared memory is broadcast to all threads in a warp. This Best Practices Guide is a manual to help developers obtain the best performance from NVIDIA CUDA GPUs. 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. These barriers can also be used alongside the asynchronous copy. 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. 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.