The reason shared memory is used in this example is to facilitate global memory coalescing on older CUDA devices (Compute Capability 1.1 or earlier). An application has no direct control over these bank conflicts. 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. Functions following the __functionName() naming convention map directly to the hardware level. Another important concept is the management of system resources allocated for a particular task. Making statements based on opinion; back them up with references or personal experience. CUDA shared memory writes incur unexplainable long latency, CUDA atomic function usage with volatile shared memory. (This was the default and only option provided in CUDA versions 5.0 and earlier.). Note this switch is effective only on single-precision floating point. 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). The host code in Zero-copy host code shows how zero copy is typically set up. By comparison, the smallest executable unit of parallelism on a CUDA device comprises 32 threads (termed a warp of threads). With UVA, the host memory and the device memories of all installed supported devices share a single virtual address space. Dealing with relocatable objects is not yet supported, therefore the cuLink* set of APIs in the CUDA driver will not work with enhanced compatibility. An explicit __syncwarp() can be used to guarantee that the warp has reconverged for subsequent instructions. It would have been more so if adjacent warps had not exhibited such a high degree of reuse of the over-fetched cache lines. This approach will greatly improve your understanding of effective programming practices and enable you to better use the guide for reference later. Bandwidth is best served by using as much fast memory and as little slow-access memory as possible. It enables GPU threads to directly access host memory. Overall, best performance is achieved when using asynchronous copies with an element of size 8 or 16 bytes. Table 2. Performance benefits can be more readily achieved when this ratio is higher. While NVIDIA GPUs are frequently associated with graphics, they are also powerful arithmetic engines capable of running thousands of lightweight threads in parallel. 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.) The effective bandwidth can vary by an order of magnitude depending on the access pattern for each type of memory. As a useful side effect, this strategy will allow us a means to reduce code duplication should we wish to include both CPU and GPU execution paths in our application: if the bulk of the work of our CUDA kernels is done in __host__ __device__ functions, we can easily call those functions from both the host code and the device code without duplication. By using new CUDA versions, users can benefit from new CUDA programming model APIs, compiler optimizations and math library features. Using a profiler, the developer can identify such hotspots and start to compile a list of candidates for parallelization. Along with the increased memory capacity, the bandwidth is increased by 72%, from 900 GB/s on Volta V100 to 1550 GB/s on A100. For those exponentiations where the exponent is not exactly representable as a floating-point number, such as 1/3, this can also provide much more accurate results, as use of pow() magnifies the initial representational error. 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(). The performance of the sliding-window benchmark with fixed hit-ratio of 1.0. An example would be modeling how two molecules interact with each other, where the molecule sizes are fixed. For devices of compute capability 8.0 (i.e., A100 GPUs) shared memory capacity per SM is 164 KB, a 71% increase compared to V100s capacity of 96 KB. One of several factors that determine occupancy is register availability. 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. Recall that the initial assess step allowed the developer to determine an upper bound for the potential speedup attainable by accelerating given hotspots. The application will then enumerate these devices as device 0 and device 1, respectively. 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. If the shared memory array size is known at compile time, as in the staticReverse kernel, then we can explicitly declare an array of that size, as we do with the array s. In this kernel, t and tr are the two indices representing the original and reverse order, respectively. Instead, strategies can be applied incrementally as they are learned. Low Priority: Make it easy for the compiler to use branch predication in lieu of loops or control statements. Hardware Acceleration for Split Arrive/Wait Barrier, 1.4.1.4. (The performance advantage sinpi() has over sin() is due to simplified argument reduction; the accuracy advantage is because sinpi() multiplies by only implicitly, effectively using an infinitely precise mathematical rather than a single- or double-precision approximation thereof.). 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. Adjust kernel launch configuration to maximize device utilization. It is however usually more effective to use a high-level programming language such as C++. .Y stands for the minor version - Introduction of new APIs, deprecation of old APIs, and source compatibility might be broken but binary compatibility is maintained. 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. Computing a row of a tile. For global memory accesses, this actual throughput is reported by the Global Load Throughput and Global Store Throughput values. An Efficient Matrix Transpose in CUDA C/C++, How to Access Global Memory Efficiently in CUDA C/C++ Kernels, How to Access Global Memory Efficiently in CUDA Fortran Kernels, Top Video Streaming and Conferencing Sessions at NVIDIA GTC 2023, Top Cybersecurity Sessions at NVIDIA GTC 2023, Top Conversational AI Sessions at NVIDIA GTC 2023, Top AI Video Analytics Sessions at NVIDIA GTC 2023, Top Data Science Sessions at NVIDIA GTC 2023. 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). 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 NVIDIA A100 GPU supports shared memory capacity of 0, 8, 16, 32, 64, 100, 132 or 164 KB per SM. These results should be compared with those in Table 2. Prior to UVA, an application had to keep track of which pointers referred to device memory (and for which device) and which referred to host memory as a separate bit of metadata (or as hard-coded information in the program) for each pointer. This typically involves arithmetic on large data sets (such as matrices) where the same operation can be performed across thousands, if not millions, of elements at the same time. From the performance chart, the following observations can be made for this experiment. 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. 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. To execute any CUDA program, there are three main steps: Copy the input data from host memory to device memory, also known as host-to-device transfer. However, if multiple threads requested addresses map to the same memory bank, the accesses are serialized. 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. Because the data is not cached on the GPU, mapped pinned memory should be read or written only once, and the global loads and stores that read and write the memory should be coalesced. Therefore, to get the largest speedup for a fixed problem size, it is worthwhile to spend effort on increasing P, maximizing the amount of code that can be parallelized. The results of these calculations can frequently differ from pure 64-bit operations performed on the CUDA device. This metric is occupancy. This cost has several ramifications: The complexity of operations should justify the cost of moving data to and from the device. Support for TF32 Tensor Core, through HMMA instructions. This is evident from the saw tooth curves. Local memory is so named because its scope is local to the thread, not because of its physical location. Shared memory is magnitudes faster to access than global memory. When the latter is much lower than the former, design or implementation details are likely to reduce bandwidth, and it should be the primary goal of subsequent optimization efforts to increase it. This section examines the functionality, advantages, and pitfalls of both approaches. The types of operations are an additional factor, as additions have different complexity profiles than, for example, trigonometric functions. For devices of compute capability 2.0, the warp size is 32 threads and the number of banks is also 32. 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. On devices of compute capability 2.x and 3.x, each multiprocessor has 64KB of on-chip memory that can be partitioned between L1 cache and shared memory. CUDA supports several compatibility choices: First introduced in CUDA 10, the CUDA Forward Compatible Upgrade is designed to allow users to get access to new CUDA features and run applications built with new CUDA releases on systems with older installations of the NVIDIA datacenter driver. 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. With each generation of NVIDIA processors, new features are added to the GPU that CUDA can leverage. 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. It can be simpler to view N as a very large number, which essentially transforms the equation into \(S = 1/(1 - P)\). For example, consider the following code: Here, the sub-expression stride*i could overflow a 32-bit integer, so if i is declared as unsigned, the overflow semantics prevent the compiler from using some optimizations that might otherwise have applied, such as strength reduction. For applications that need additional functionality or performance beyond what existing parallel libraries or parallelizing compilers can provide, parallel programming languages such as CUDA C++ that integrate seamlessly with existing sequential code are essential. 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. 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. Finally, particular attention must be paid to control flow instructions due to the SIMT (single instruction multiple thread) nature of the device. 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(). UVA is also a necessary precondition for enabling peer-to-peer (P2P) transfer of data directly across the PCIe bus or NVLink for supported GPUs in supported configurations, bypassing host memory. 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. math libraries or deep learning frameworks) do not have a direct dependency on the CUDA runtime, compiler or driver. In particular, there is no register-related reason to pack data into vector data types such as float4 or int4 types. Compatibility of the CUDA platform is thus intended to address a few scenarios: NVIDIA driver upgrades to systems with GPUs running in production for enterprises or datacenters can be complex and may need advance planning. The remainder of the kernel code is identical to the staticReverse() kernel. Consequently, its important to understand the characteristics of the architecture. Note that when a thread block allocates more registers than are available on a multiprocessor, the kernel launch fails, as it will when too much shared memory or too many threads are requested.