The Speed Booster ULTRA 0.71x reduces the crop factor of the BMPCC4K camera as shown in the above table. The new design for BMPCC4K makes very effective use of exotic materials at the furthest limit of glassmaking technology, and as a result is almost perfectly corrected for use with all full-frame SLR lenses regardless of aperture or exit pupil distance. The Speed Booster ULTRA 0.71x will also work extremely well with many DX and APS-C format lenses, provided the image circle provided by the lens is large enough. Optical performance of the new Speed Boosters is so good that the MTF of any lens attached to it will be improved. Even the latest generation of ultra-high performance SLR lenses such as the Zeiss Otus series can be improved by adding a Speed Booster ULTRA 0.71x.
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The new Speed Booster XL 0.64x reduces the full-frame crop factor of the BMPCC4K as shown in the table further above. In addition, the speed of any attached lens is increased by 11/3 stops, with a maximum output aperture of f/0.80 when an f/1.2 lens is used. For example, a 50mm f/1.2 becomes a 32mm f/0.80, which is the fastest aperture available for Blackmagic cameras.
In particular, the optimization section of this guide assumes that you have already successfully downloaded and installed the CUDA Toolkit (if not, please refer to the relevant CUDA Installation Guide for your platform) and that you have a basic familiarity with the CUDA C++ programming language and environment (if not, please refer to the CUDA C++ Programming Guide).
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.
For example, many kernels have complex addressing logic for accessing memory in addition to their actual computation. If we validate our addressing logic separately prior to introducing the bulk of the computation, then this will simplify any later debugging efforts. (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.)
If from any of the four 32-byte segments only a subset of the words are requested (e.g. if several threads had accessed the same word or if some threads did not participate in the access), the full segment is fetched anyway. 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.
In Using shared memory to improve the global memory load efficiency in matrix multiplication, each element in a tile of A is read from global memory only once, in a fully coalesced fashion (with no wasted bandwidth), to shared memory. Within each iteration of the for loop, a value in shared memory is broadcast to all threads in a warp. Instead of a __syncthreads()synchronization barrier call, a __syncwarp() is sufficient after reading the tile of A into shared memory because only threads within the warp that write the data into shared memory read this data. This kernel has an effective bandwidth of 144.4 GB/s on an NVIDIA Tesla V100. This illustrates the use of the shared memory as a user-managed cache when the hardware L1 cache eviction policy does not match up well with the needs of the application or when L1 cache is not used for reads from global memory.
As mentioned in Occupancy, higher occupancy does not always equate to better performance. For example, improving occupancy from 66 percent to 100 percent generally does not translate to a similar increase in performance. A lower occupancy kernel will have more registers available per thread than a higher occupancy kernel, which may result in less register spilling to local memory; in particular, with a high degree of exposed instruction-level parallelism (ILP) it is, in some cases, possible to fully cover latency with a low occupancy.
Both the CUDA driver and the CUDA runtime are not source compatible across the different SDK releases. APIs can be deprecated and removed. Therefore, an application that compiled successfully on an older version of the toolkit may require changes in order to compile against a newer version of the toolkit.
When our CUDA 11.1 application (i.e. cudart 11.1 is statically linked) is run on the system, we see that it runs successfully even when the driver reports a 11.0 version - that is, without requiring the driver or other toolkit components to be updated on the system.
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. The cudaGetDeviceCount() function can be used to query for the number of available devices. Like all CUDA Runtime API functions, this function will fail gracefully and return cudaErrorNoDevice to the application if there is no CUDA-capable GPU or cudaErrorInsufficientDriver if there is not an appropriate version of the NVIDIA Driver installed. If cudaGetDeviceCount() reports an error, the application should fall back to an alternative code path.
Maximizing parallel execution starts with structuring the algorithm in a way that exposes as much parallelism as possible. Once the parallelism of the algorithm has been exposed, it needs to be mapped to the hardware as efficiently as possible. This is done by carefully choosing the execution configuration of each kernel launch. 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.
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