- Opencl benchmark tool drivers#
- Opencl benchmark tool portable#
- Opencl benchmark tool software#
- Opencl benchmark tool code#
- Opencl benchmark tool free#
Operations that take advantage of GPU functionality will usually run better on a GPU. A lot depends on how various kernels are written and executed, as well as the type of operations being performed. Some applications will do better on a multicore CPU, while others will do better with GPUs. Having lots of cores can help in some applications, but any speed-up tends to be application-specific. Typically, a GPU will have many more cores than even a multicore CPU. OpenCL will run better on a GPU than a CPU. Typically, developers targeting Nvidia GPUs will choose CUDA or OpenCL for their projects.Ĥ.
Opencl benchmark tool drivers#
Nvidia device drivers for Nvidia hardware support CUDA, as well as OpenCL. In addition, the CUDA Toolkit includes additional libraries like cuDNN for deep learning, cuFFT, cuBLAS, and NPP (NVIDIA Performance Primitives for imaging).
Opencl benchmark tool code#
Both use the kernel approach to partitioning code, and both support C and C++ for kernel code.ĬUDA is an NVidia architecture that specifically targets Nvidia GPU hardware, but it can also generate code for some CPUs, such as x86 platforms providing similar functionality as OpenCL. Nvidia’s CUDA is similar to OpenCL, but they are distinct. CUDA is just Nvidia’s version of OpenCL.įalse. FPGAs have the advantage of performing many operations in parallel.ģ. The results can then be extracted or made available to another kernel in the same fashion, since OpenCL operates on a CPU or GPU.
Opencl benchmark tool software#
There’s also software support to match that moves data between a host and the FPGA and to initiate kernel execution in the FPGA.Įssentially, data is placed into the FPGA’s memory where a kernel has access to it. Altera’s SDK for OpenCL has an OpenCL compiler that generates an FPGA configuration that includes supporting the configuration for what is essentially the OpenCL runtime. This approach is a bit more static because the kernels are implemented in a FPGA, and its configuration doesn’t normally change over time. An OpenCL application can run on a single-core CPU, but normally multiple-core systems are the target to get more overall performance from a system. The compiler dictates what targets it will support. It can generate code for target hardware that can include CPUs, GPUs, or a mix of the two. OpenCL is a specification that’s implemented by an OpenCL compiler.
Opencl benchmark tool portable#
Kernels are not portable across different hardware, but the source code is. The code for a kernel may be shared among cores in a shared memory system, or it may be copied to a node as in a cluster system. A kernel runs until it terminates, and the resulting data can be used in future computations. The runtime schedules kernels and their matching data to run on the hardware that normally consists of multiple cores, thereby potentially providing a speed-up of the application. Even determining how functions will be implemented in a kernel will affect performance.
Opencl benchmark tool free#
This does not come free simply by using an OpenCL compiler. Part of the challenge in writing OpenCL code is to take advantage of the extensions and constructs for parallelizing computation, since this is now OpenCL speeds up execution of an application. OpenCL kernels are written in C or C++ (as of OpenCL 2.2). Application code is written in the form of small kernels that are then scheduled to execute by the runtime (see figure). OpenCL is a language specification, a runtime API, and a software framework that includes an OpenCL compiler and matching runtime. I just need to write C code to use OpenCL. They may be the same or different, depending on the application and its current state.ġ. A multicore system is able to run multiple kernels at the same time. The resulting data can be passed to the next kernel that requires it. An OpenCL program is compiled into kernels or nuggets of code, which can be applied to data.
Likewise, those simply using OpenCL-based software will not care other than to have their software run properly on OpenCL-supported hardware. Those already writing OpenCL already know how it works and will probably not find anything new in the following myths. It is often a checkbox item for hardware feature sets, and developers can utilize OpenCL-based software without knowing what is involved by either the software or the hardware that it is running on. OpenCL has wide support on both the hardware and software side. Applications are written in a variant of C, and the latest OpenCL 2.2 brings a static subset of C++14 into the mix. OpenCL is an open, cross-platform, parallel-computing standard from the Khronos Group. This file type includes high resolution graphics and schematics when applicable.
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