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. On Wednesday, February 19, 2020, NVIDIA will present part 2 of a 9-part CUDA Training Series titled "CUDA Shared Memory". CUDA compatibility allows users to update the latest CUDA Toolkit software (including the compiler, libraries, and tools) without requiring update to the entire driver stack. This optimization is especially important for global memory accesses, because latency of access costs hundreds of clock cycles. A slightly related but important topic is one of application binary compatibility across GPU architectures in CUDA. 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. 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. For devices of compute capability 2.0, the warp size is 32 threads and the number of banks is also 32. In the next post I will continue our discussion of shared memory by using it to optimize a matrix transpose. In the kernel launch, specify the total shared memory needed, as in the following. High Priority: To get the maximum benefit from CUDA, focus first on finding ways to parallelize sequential code. However, if multiple addresses of a memory request map to the same memory bank, the accesses are serialized. Does there exist a square root of Euler-Lagrange equations of a field? When we can, we should use registers. 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.) 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. All CUDA threads can access it for read and write. The application should also maximize parallel execution at a higher level by explicitly exposing concurrent execution on the device through streams, as well as maximizing concurrent execution between the host and the device. To target specific versions of NVIDIA hardware and CUDA software, use the -arch, -code, and -gencode options of nvcc. Prefer shared memory access where possible. 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. (This was the default and only option provided in CUDA versions 5.0 and earlier.). They produce equivalent results. Therefore, it is best to avoid multiple contexts per GPU within the same CUDA application. If it has, it will be declared using the .local mnemonic and accessed using the ld.local and st.local mnemonics. Applications compiled with CUDA toolkit versions as old as 3.2 will run on newer drivers. 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. CUDA: Explainer of a kernel with 2D blocks, shared memory, atomics 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. More details about the compute capabilities of various GPUs are in CUDA-Enabled GPUs and Compute Capabilities of the CUDA C++ Programming Guide. But this technique is still useful for other access patterns, as Ill show in the next post.). For devices of compute capability 8.0 (i.e., A100 GPUs) the maximum shared memory per thread block is 163 KB. When using a shared or static library, follow the release notes of said library to determine if the library supports minor version compatibility. They are faster but provide somewhat lower accuracy (e.g., __sinf(x) and __expf(x)). The formulas in the table below are valid for x >= 0, x != -0, that is, signbit(x) == 0. We will note some of them later on in the document. 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. When a CUDA kernel accesses a data region in the global memory repeatedly, such data accesses can be considered to be persisting. It can be copied into the same directory as the application executable or into a subdirectory of that installation path. 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. Medium Priority: Prefer faster, more specialized math functions over slower, more general ones when possible. CUDA driver - User-mode driver component used to run CUDA applications (e.g. When choosing the first execution configuration parameter-the number of blocks per grid, or grid size - the primary concern is keeping the entire GPU busy. The kernel also uses the default stream, and it will not begin execution until the memory copy completes; therefore, no explicit synchronization is needed. This makes the code run faster at the cost of diminished precision and accuracy. Misaligned sequential addresses that fall within five 32-byte segments, Memory allocated through the CUDA Runtime API, such as via cudaMalloc(), is guaranteed to be aligned to at least 256 bytes. See the CUDA C++ Programming Guide for details. Not using intermediate registers can help reduce register pressure and can increase kernel occupancy. Computing a row of a tile. This action leads to a load of eight L2 cache segments per warp on the Tesla V100 (compute capability 7.0). When linking with dynamic libraries from the toolkit, the library must be equal to or newer than what is needed by any one of the components involved in the linking of your application. In a typical system, thousands of threads are queued up for work (in warps of 32 threads each). No license, either expressed or implied, is granted under any NVIDIA patent right, copyright, or other NVIDIA intellectual property right under this document. Since shared memory is shared amongst threads in a thread block, it provides a mechanism for threads to cooperate. On devices of compute capability 6.0 or higher, L1-caching is the default, however the data access unit is 32-byte regardless of whether global loads are cached in L1 or not. The compute capability describes the features of the hardware and reflects the set of instructions supported by the device as well as other specifications, such as the maximum number of threads per block and the number of registers per multiprocessor. Using asynchronous copies does not use any intermediate register. The maximum number of registers per thread can be set manually at compilation time per-file using the -maxrregcount option or per-kernel using the __launch_bounds__ qualifier (see Register Pressure). Shared Memory in Matrix Multiplication (C=AAT), 9.2.3.4. 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). The NVIDIA Ampere GPU architecture increases the capacity of the L2 cache to 40 MB in Tesla A100, which is 7x larger than Tesla V100. GPUs with compute capability 8.6 support shared memory capacity of 0, 8, 16, 32, 64 or 100 KB per SM. 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. 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. These results should be compared with those in Table 2. Any PTX device code loaded by an application at runtime is compiled further to binary code by the device driver. Although the CUDA Runtime provides the option of static linking, some libraries included in the CUDA Toolkit are available only in dynamically-linked form. Therefore, it is important to be sure to compare values of like precision and to express the results within a certain tolerance rather than expecting them to be exact. How to manage this resource utilization is discussed in the final sections of this chapter. Production code should, however, systematically check the error code returned by each API call and check for failures in kernel launches by calling cudaGetLastError(). This is particularly beneficial to kernels that frequently call __syncthreads(). Coalesced using shared memory to store a tile of A, Using shared memory to eliminate redundant reads of a tile of B. The __pipeline_wait_prior(0) will wait until all the instructions in the pipe object have been executed. Strong scaling is usually equated with Amdahls Law, which specifies the maximum speedup that can be expected by parallelizing portions of a serial program. Before tackling other hotspots to improve the total speedup, the developer should consider taking the partially parallelized implementation and carry it through to production. Whether a device has this capability is indicated by the concurrentKernels field of the cudaDeviceProp structure (or listed in the output of the deviceQuery CUDA Sample). If all threads of a warp access the same location, then constant memory can be as fast as a register access. 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. 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. This value is expressed in milliseconds and has a resolution of approximately half a microsecond. If a single block needs to load all queues, then all queues will need to be placed in global memory by their respective blocks. CUDA Memory Global Memory We used global memory to hold the functions values. The primary differences are in threading model and in separate physical memories: Execution pipelines on host systems can support a limited number of concurrent threads. See the nvidia-smi documenation for details. A useful technique to determine the sensitivity of performance to occupancy is through experimentation with the amount of dynamically allocated shared memory, as specified in the third parameter of the execution configuration. One of the key differences is the fused multiply-add (FMA) instruction, which combines multiply-add operations into a single instruction execution. CUDA-GDB is a port of the GNU Debugger that runs on Linux and Mac; see: https://developer.nvidia.com/cuda-gdb. Using the CUDA Occupancy Calculator to project GPU multiprocessor occupancy. Armed with this knowledge, the developer can evaluate these bottlenecks for parallelization and start to investigate GPU acceleration. cudaDeviceSynchronize()blocks the calling CPU thread until all CUDA calls previously issued by the thread are completed. Unified memory: supports seamless access to buffers or objects from multiple GPUs and CPUs. Understanding Scaling discusses the potential benefit we might expect from such parallelization. For example, a matrix multiplication of the same matrices requires N3 operations (multiply-add), so the ratio of operations to elements transferred is O(N), in which case the larger the matrix the greater the performance benefit. The C++ host code generated by nvcc utilizes the CUDA Runtime, so applications that link to this code will depend on the CUDA Runtime; similarly, any code that uses the cuBLAS, cuFFT, and other CUDA Toolkit libraries will also depend on the CUDA Runtime, which is used internally by these libraries. Because the default stream, stream 0, exhibits serializing behavior for work on the device (an operation in the default stream can begin only after all preceding calls in any stream have completed; and no subsequent operation in any stream can begin until it finishes), these functions can be used reliably for timing in the default stream. Shared memory enables cooperation between threads in a block. The host code in Zero-copy host code shows how zero copy is typically set up. sm_80) rather than a virtual architecture (e.g. 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. High Priority: To maximize developer productivity, profile the application to determine hotspots and bottlenecks. 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. These include threading issues, unexpected values due to the way floating-point values are computed, and challenges arising from differences in the way CPU and GPU processors operate. Another important concept is the management of system resources allocated for a particular task. 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. Kernels can be written using the CUDA instruction set architecture, called PTX, which is described in the PTX reference manual. As PTX is compiled by the CUDA driver, new toolchains will generate PTX that is not compatible with the older CUDA driver. Strong Scaling and Amdahls Law describes strong scaling, which allows us to set an upper bound for the speedup with a fixed problem size.