Moreover, in such cases, the argument-reduction code uses local memory, which can affect performance even more because of the high latency of local memory. By comparison, the smallest executable unit of parallelism on a CUDA device comprises 32 threads (termed a warp of threads). APIs can be deprecated and removed. Consequently, the order in which arithmetic operations are performed is important. 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. Is it possible to share a Cuda context between applications - Introduction CUDA is a parallel computing platform and programming model created by Nvidia. CUDA: Shared memory allocation with overlapping borders 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 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. See Math Libraries. A copy kernel that illustrates misaligned accesses. Furthermore, this file should be installed into the @rpath of the application; see Where to Install Redistributed CUDA Libraries. 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. Flow control instructions (if, switch, do, for, while) can significantly affect the instruction throughput by causing threads of the same warp to diverge; that is, to follow different execution paths. For more information on the Runtime API, refer to CUDA Runtime of the CUDA C++ Programming Guide. The programmer can also control loop unrolling using. Indicate whether the NVIDIA driver stays loaded when no applications are connected to the GPU. It is best to enable this option in most circumstances. Code that transfers data for brief use by a small number of threads will see little or no performance benefit. The number of copy engines on a GPU is given by the asyncEngineCount field of the cudaDeviceProp structure, which is also listed in the output of the deviceQuery CUDA Sample. 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. 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. 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. The hardware splits a conflicting memory request into as many separate conflict-free requests as necessary, decreasing the effective bandwidth by a factor equal to the number of colliding memory requests. Any PTX device code loaded by an application at runtime is compiled further to binary code by the device driver. It should also be noted that the CUDA math librarys complementary error function, erfcf(), is particularly fast with full single-precision accuracy. Reading from a texture while writing to its underlying global memory array in the same kernel launch should be avoided because the texture caches are read-only and are not invalidated when the associated global memory is modified. An application can also use the Occupancy API from the CUDA Runtime, e.g. In general, they should be avoided, because compared to peak capabilities any architecture processes these memory access patterns at a low efficiency. 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 example below shows how to use the access policy window on a CUDA stream. Does there exist a square root of Euler-Lagrange equations of a field? Understanding Scaling discusses the potential benefit we might expect from such parallelization. Devices of compute capability 3.x have configurable bank size, which can be set using cudaDeviceSetSharedMemConfig() to either four bytes (cudaSharedMemBankSizeFourByte, thedefault) or eight bytes (cudaSharedMemBankSizeEightByte). CUDA Memory Rules Currently can only transfer data from host to global (and constant memory) and not host directly to shared. See the nvidia-smi documenation for details. For devices of compute capability 2.x, there are two settings, 48KBshared memory / 16KB L1 cache, and 16KB shared memory / 48KB L1 cache. The cudaGetDeviceCount() function can be used to query for the number of available devices. 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. Dont expose ABI structures that can change. Access to shared memory is much faster than global memory access because it is located on a chip. Loop Counters Signed vs. Unsigned, 11.1.5. Weak scaling is often equated with Gustafsons Law, which states that in practice, the problem size scales with the number of processors. This should be our first candidate function for parallelization. Asynchronous transfers enable overlap of data transfers with computation in two different ways. In Unoptimized handling of strided accesses to global memory, the row-th, col-th element of C is obtained by taking the dot product of the row-th and col-th rows of A. A subset of CUDA APIs dont need a new driver and they can all be used without any driver dependencies. 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. rev2023.3.3.43278. 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. The list of active processes running on the GPU is reported, along with the corresponding process name/ID and allocated GPU memory. Otherwise, five 32-byte segments are loaded per warp, and we would expect approximately 4/5th of the memory throughput achieved with no offsets. After each change is made, ensure that the results match using whatever criteria apply to the particular algorithm. 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. When a CUDA kernel accesses a data region in the global memory repeatedly, such data accesses can be considered to be persisting. "After the incident", I started to be more careful not to trip over things. 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. Shared memory can also be used to avoid uncoalesced memory accesses by loading and storing data in a coalesced pattern from global memory and then reordering it in shared memory. When multiple threads in a block use the same data from global memory, shared memory can be used to access the data from global memory only once. Best practices suggest that this optimization be performed after all higher-level optimizations have been completed. 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. One method for doing so utilizes shared memory, which is discussed in the next section. In a typical system, thousands of threads are queued up for work (in warps of 32 threads each). The context is explicit in the CUDA Driver API but is entirely implicit in the CUDA Runtime API, which creates and manages contexts automatically. While multiple contexts (and their associated resources such as global memory allocations) can be allocated concurrently on a given GPU, only one of these contexts can execute work at any given moment on that GPU; contexts sharing the same GPU are time-sliced. 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. 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. Choosing execution parameters is a matter of striking a balance between latency hiding (occupancy) and resource utilization. Because L2 cache is on-chip, it potentially provides higher bandwidth and lower latency accesses to global memory. Please see the MSDN documentation for these routines for more information. 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. We can then launch this kernel onto the GPU and retrieve the results without requiring major rewrites to the rest of our application. Medium Priority: Use shared memory to avoid redundant transfers from global memory. The major and minor revision numbers of the compute capability are shown on the seventh line of Figure 16. 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. 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. 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). 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. The NVIDIA Ampere GPU architecture adds hardware acceleration for copying data from global memory to shared memory. As an exception, scattered writes to HBM2 see some overhead from ECC but much less than the overhead with similar access patterns on ECC-protected GDDR5 memory. Note that the performance improvement is not due to improved coalescing in either case, but to avoiding redundant transfers from global memory. One of the key differences is the fused multiply-add (FMA) instruction, which combines multiply-add operations into a single instruction execution. 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. Devices of compute capability 3.x allow a third setting of 32KB shared memory / 32KB L1 cache which can be obtained using the optioncudaFuncCachePreferEqual. If sequential threads in a warp access memory that is sequential but not aligned with a 32-byte segment, five 32-byte segments will be requested, as shown in Figure 4. Providing the two argument version of __launch_bounds__(maxThreadsPerBlock,minBlocksPerMultiprocessor) can improve performance in some cases. 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. An optimized handling of strided accesses using coalesced reads from global memory uses the shared transposedTile to avoid uncoalesced accesses in the second term in the dot product and the shared aTile technique from the previous example to avoid uncoalesced accesses in the first term. One of the main reasons a new toolchain requires a new minimum driver is to handle the JIT compilation of PTX code and the JIT linking of binary code. To measure performance accurately, it is useful to calculate theoretical and effective bandwidth. The results of these optimizations are summarized in Table 3. For example, on a device of compute capability 7.0, a kernel with 128-thread blocks using 37 registers per thread results in an occupancy of 75% with 12 active 128-thread blocks per multi-processor, whereas a kernel with 320-thread blocks using the same 37 registers per thread results in an occupancy of 63% because only four 320-thread blocks can reside on a multiprocessor. These are the same contexts used implicitly by the CUDA Runtime when there is not already a current context for a thread. Instead, strategies can be applied incrementally as they are learned. The formulas in the table below are valid for x >= 0, x != -0, that is, signbit(x) == 0. This means that in one of these devices, for a multiprocessor to have 100% occupancy, each thread can use at most 32 registers. (This was the default and only option provided in CUDA versions 5.0 and earlier.). This chapter examines issues that can affect the correctness of returned data and points to appropriate solutions. The latter case can be avoided by using single-precision floating-point constants, defined with an f suffix such as 3.141592653589793f, 1.0f, 0.5f. Sometimes, the compiler may unroll loops or optimize out if or switch statements by using branch predication instead. Performance benefits can be more readily achieved when this ratio is higher. The texture cache is optimized for 2D spatial locality, so threads of the same warp that read texture addresses that are close together will achieve best performance. 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. Choosing the execution configuration parameters should be done in tandem; however, there are certain heuristics that apply to each parameter individually. Now I have some problems. Certain hardware features are not described by the compute capability. Conversion to Warp isn't possible for Week 6-7 because there is no support for shared memory or block level synchronization. Register pressure occurs when there are not enough registers available for a given task. One or more compute capability versions can be specified to the nvcc compiler while building a file; compiling for the native compute capability for the target GPU(s) of the application is important to ensure that application kernels achieve the best possible performance and are able to use the features that are available on a given generation of GPU. The programming guide to using the CUDA Toolkit to obtain the best performance from NVIDIA GPUs. 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 variant of the previous matrix multiplication can be used to illustrate how strided accesses to global memory, as well as shared memory bank conflicts, are handled.
cuda shared memory between blocks