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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a>
Heterogeneous architecture has been widely adopted in various computing systems, from mobile devices to servers. However, optimizing the performance for such platforms remains challenging in three aspects: the control flow divergence decreases the utilization of SIMD components, the significant memory copy overhead between computing devices consuming precious memory bandwidth and the load imbalance that degrades the overall performance. In this paper, we proposed three methodologies:<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-662-44917-2_63">doi:10.1007/978-3-662-44917-2_63</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kisoz3mahvddnda5kpxv5vwjsi">fatcat:kisoz3mahvddnda5kpxv5vwjsi</a> </span>
more »... e Feedback, Dynamic Task Partitioning and Heterogeneous Runtime that work collaboratively to overcome the aforesaid problems. We adopted and implemented these methodologies in a heterogeneous runtime library derived from Intel Embree and compared the performance results of the two frameworks running Ray-Tracing on various scenes. Experiment results have shown that the performance gain from the proposed methods is significant, especially in complex scenes with a large amount of objects or with large input data sizes the CPU cannot handle efficiently. Due to the performance and power efficiency potentials of GPGPU and heterogeneous systems, a wide variety of applications, which include molecular simulation, fluid dynamics, biomedical image processing and computer vision, have been developed by leveraging the aforementioned programming models. However, for this type of heterogeneous configuration, many challenges remain before the performance potential can be fully unleashed. Despite significant advantages of GPU programming, writing high-performance heterogeneous programs still require programmers to be familiar with GPU architecture. The performance of a heterogeneous program is significantly influenced by how the computations are mapped into threads and how those threads are scheduled onto distinct cores, the usage of GPU registers and memory hierarchy, the synchronization among all threads and data access, the data transfer between host and GPU memories and the control flow branch divergence issue. In short, a program which benefits from GPGPU must contain explicit parallelism, high regularity and data reuse. The irregularities in an application may throttle the expected performance of the GPU by as much as an order of magnitude. The terms regular and irregular are often used in compiler literature. For example, in regular code, control flow and data memory references are not data dependent. Dense matrix multiplication operations are good examples of regular code. On the other hand, in irregular code, both control flow and data memory references could be data dependent. For example, graph-based applications are
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