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Heterogeneous platforms composed of multi-core CPUs and different types of accelerators, like GPUs and Xeon Phi, are becoming popular for data parallel applications. The heterogeneity of the hardware mix and the diversity of the applications pose significant challenges to exploiting such platforms. In this situation, an effective workload partitioning between processing units is critically important...
Heterogeneous platforms integrating different types of processing units (such as multi-core CPUs and GPUs) are in high demand in high performance computing. Existing studies have shown that using heterogeneous platforms can improve application performance and hardware utilization. However, systematic methods to design, implement, and map applications to efficiently use heterogeneous computing resources...
GPUs are widely used to accelerate data-parallel applications. However, while the GPU processing capability is enhanced in each generation, the CPU computing power is also increased by adding more cores and widening vector units. Compared to the rapid development of GPUs and CPUs, the bandwidth of the data transfer between GPUs and the host CPU grows much slower, resulting in a data-transfer wall...
Recent parallel architectures are equipped with local memory, which simplifies hardware design at the cost of increased program complexity due to explicit management. To simplify this extra-burden that programmers have, we introduce an easy-to-use API, ELMO, that improves productivity while preserving high performance of local memory operations. Specifically, ELMO is a generic API that covers different...
Real-time stereo matching, which is important in many applications like self-driving cars and 3-D scene reconstruction, requires large computation capability and high memory bandwidth. The most time-consuming part of stereo-matching algorithms is the aggregation of information (i.e. costs) over local image regions. In this paper, we present a generic representation and suitable implementations for...
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