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- W4367358231 abstract "In this work, we demonstrate the implementation of two algorithms: the addition of two vectors and the matrix multiplication, using the C + + programming language, the Open Computing Language (OpenCL) framework, and its kernel optimization metrics. In embedded systems, the processing time of most algorithms is a challenge that we attempt to address in this paper by switching from sequential to parallel implementation using high-level synthesis (HLS) tools. The main purpose of the work has been to propose optimization methods for the heterogeneous embedded CPU-GPU architecture. The primary goals of these methods are to reduce resource use and processing time. The methods applied in our work are based on global memory for communication between the host and the device. We also have a local memory in our system that enables us to restrict access to the global memory, which gives us an advantage in terms of latency and processing speed. We have specifications for the number of workgroups, unrolling loops, and computing units that allow us to optimize both the amount of time it takes to process data and the number of resources used by a kernel. The results demonstrate that the parallel implementation of our algorithms, through the use of OpenCL and these kernel optimization metrics, provides better processing speed than the sequential implementation." @default.
- W4367358231 created "2023-04-30" @default.
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- W4367358231 date "2023-01-01" @default.
- W4367358231 modified "2023-09-26" @default.
- W4367358231 title "OpenCL Kernel Optimization Metrics for CPU-GPU Architecture" @default.
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- W4367358231 doi "https://doi.org/10.1007/978-3-031-29857-8_77" @default.
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