In this example, the deviceQuery sample is compiled with CUDA 11.1 and is run on a system with R418. (See Data Transfer Between Host and Device.) Choosing execution parameters is a matter of striking a balance between latency hiding (occupancy) and resource utilization. As a result, all modern processors require parallel code in order to achieve good utilization of their computational power. A kernel to illustrate non-unit stride data copy. For devices of compute capability 2.x, there are two settings, 48KBshared memory / 16KB L1 cache, and 16KB shared memory / 48KB L1 cache. In this scenario, CUDA initialization returns an error due to the minimum driver requirement. To illustrate the effect of strided access on effective bandwidth, see the kernel strideCopy() in A kernel to illustrate non-unit stride data copy, which copies data with a stride of stride elements between threads from idata to odata. At a minimum, you would need some sort of selection process that can access the heads of each queue. Having completed the GPU acceleration of one or more components of the application it is possible to compare the outcome with the original expectation. The operating system must swap threads on and off CPU execution channels to provide multithreading capability. In the example above, we can clearly see that the function genTimeStep() takes one-third of the total running time of the application. 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. The types of operations are an additional factor, as additions have different complexity profiles than, for example, trigonometric functions. To get a closer match between values, set the x86 host processor to use regular double or single precision (64 bits and 32 bits, respectively). Some calculations use 10243 instead of 109 for the final calculation. Its result will often differ slightly from results obtained by doing the two operations separately. Once we have located a hotspot in our applications profile assessment and determined that custom code is the best approach, we can use CUDA C++ to expose the parallelism in that portion of our code as a CUDA kernel. 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. A minimum of 64 threads per block should be used, and only if there are multiple concurrent blocks per multiprocessor. Is it possible to create a concave light? Recommendations for building a minor-version compatible library, 15.4.1.5. CUDA reserves 1 KB of shared memory per thread block. These recommendations are categorized by priority, which is a blend of the effect of the recommendation and its scope. Shared memory can be helpful in several situations, such as helping to coalesce or eliminate redundant access to global memory. 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. For further details on the programming features discussed in this guide, please refer to the CUDA C++ Programming Guide. NVIDIA Ampere GPU Architecture Tuning Guide, 1.4. read- only by GPU) Shared memory is said to provide up to 15x speed of global memory Registers have similar speed to shared memory if reading same address or no bank conicts. 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. However, this requires writing to shared memory in columns, and because of the use of wxw tiles in shared memory, this results in a stride between threads of w banks - every thread of the warp hits the same bank (Recall that w is selected as 32). The current GPU core temperature is reported, along with fan speeds for products with active cooling. Devices of compute capability 1.3 and higher provide native support for double-precision floating-point values (that is, values 64 bits wide). Using the CUDA Occupancy Calculator to project GPU multiprocessor occupancy. This chapter contains a summary of the recommendations for optimization that are explained in this document. For example, to compute the effective bandwidth of a 2048 x 2048 matrix copy, the following formula could be used: \(\text{Effective\ bandwidth} = \left( {\left( 2048^{2} \times 4 \times 2 \right) \div 10^{9}} \right) \div \text{time}\). The cudaDeviceEnablePeerAccess() API call remains necessary to enable direct transfers (over either PCIe or NVLink) between GPUs. To analyze performance, it is necessary to consider how warps access global memory in the for loop. 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). Automatic variables that are likely to be placed in local memory are large structures or arrays that would consume too much register space and arrays that the compiler determines may be indexed dynamically. This is advantageous with regard to both accuracy and performance. Applying Strong and Weak Scaling, 6.3.2. However, striding through global memory is problematic regardless of the generation of the CUDA hardware, and would seem to be unavoidable in many cases, such as when accessing elements in a multidimensional array along the second and higher dimensions. Sample CUDA configuration data reported by deviceQuery. See Register Pressure. All rights reserved. Timeline comparison for copy and kernel execution, Table 1. Always check the error return values on all CUDA API functions, even for functions that are not expected to fail, as this will allow the application to detect and recover from errors as soon as possible should they occur. The compiler must on occasion insert conversion instructions, introducing additional execution cycles. This document is not a commitment to develop, release, or deliver any Material (defined below), code, or functionality. Furthermore, if accesses by the threads of the warp had been permuted within or accross the four segments, still only four 32-byte transactions would have been performed by a device with compute capability 6.0 or higher. High Priority: Use the effective bandwidth of your computation as a metric when measuring performance and optimization benefits. A copy kernel that illustrates misaligned accesses. Devices of compute capability 2.0 and later support a special addressing mode called Unified Virtual Addressing (UVA) on 64-bit Linux and Windows. The cudaDeviceCanAccessPeer() can be used to determine if peer access is possible between any pair of GPUs. Shared memory is magnitudes faster to access than global memory. Non-default streams (streams other than stream 0) are required for concurrent execution because 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. Devices to be made visible to the application should be included as a comma-separated list in terms of the system-wide list of enumerable devices. A place where magic is studied and practiced? To assist with this, the CUDA Driver API provides methods to access and manage a special context on each GPU called the primary context. In fact, shared memory latency is roughly 100x lower than uncached global memory latency (provided that there are no bank conflicts between the threads, which we will examine later in this post). The effective bandwidth of this kernel is 140.2 GB/s on an NVIDIA Tesla V100.These results are lower than those obtained by the final kernel for C = AB. Increment major versions when there are ABI breaking changes such as API deprecation and modifications. 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 read-only texture memory space is cached. 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). In addition to the calculator spreadsheet, occupancy can be determined using the NVIDIA Nsight Compute Profiler. For example, we can write our CUDA kernels as a collection of many short __device__ functions rather than one large monolithic __global__ function; each device function can be tested independently before hooking them all together. // (Must be less than cudaDeviceProp::accessPolicyMaxWindowSize), // Hint for L2 cache hit ratio for persisting accesses in the num_bytes region. NVIDIA hereby expressly objects to applying any customer general terms and conditions with regards to the purchase of the NVIDIA product referenced in this document. These memory spaces include global, local, shared, texture, and registers, as shown in Figure 2. 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). A natural decomposition of the problem is to use a block and tile size of wxw threads. Missing dependencies is also a binary compatibility break, hence you should provide fallbacks or guards for functionality that depends on those interfaces. Constantly recompiling with the latest CUDA Toolkit means forcing upgrades on the end-customers of an application product. There is a total of 64 KB constant memory on a device. Is a PhD visitor considered as a visiting scholar? For 32-bit applications, the file would be cublas32_55.dll. No contractual obligations are formed either directly or indirectly by this document. The NVIDIA A100 GPU supports shared memory capacity of 0, 8, 16, 32, 64, 100, 132 or 164 KB per SM. 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. PTX programs are translated at load time to the target hardware instruction set via the JIT Compiler which is part of the CUDA driver. 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. :class table-no-stripes, Table 3. 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 first is the compute capability, and the second is the version number of the CUDA Runtime and CUDA Driver APIs. This is called just-in-time compilation (JIT). An exception is the case where all threads in a warp address the same shared memory address, resulting in a broadcast. In such cases, users or developers can still benefit from not having to upgrade the entire CUDA Toolkit or driver to use these libraries or frameworks. Your code might reflect different priority factors. Before we proceed further on this topic, its important for developers to understand the concept of Minimum Driver Version and how that may affect them. However, the set of registers (known as the register file) is a limited commodity that all threads resident on a multiprocessor must share. If this set-aside portion is not used by persistent accesses, then streaming or normal data accesses can use it. These bindings expose the same features as the C-based interface and also provide backwards compatibility. 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. Therefore, the compiler can optimize more aggressively with signed arithmetic than it can with unsigned arithmetic. When using multiple GPUs from the same application, it is recommended to use GPUs of the same type, rather than mixing hardware generations. If we validate our addressing logic separately prior to introducing the bulk of the computation, then this will simplify any later debugging efforts. Week5 + Week8 by AkeelMedina22 Pull Request #9 mmmovania/CUDA A very important performance consideration in programming for CUDA-capable GPU architectures is the coalescing of global memory accesses.
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