Accelerator design for graph analytics
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With the increase in data available online, data analysis became a significant problem in today’s datacenters. Moreover, graph analytics is one of the significant application domains in big data era. However, traditional architectures such as CPUs and Graphics Processing Units (GPUs) fail to serve the needs of graph applications. Unconventional properties of graph applications such as irregular memory accesses, load balancing, and irregular computation challenge current computing systems which are either throughput oriented or built on top of traditional locality based memory subsystems. On the other hand, an emerging technique hardware customization, can help us to overcome these problems since they are expected to be energy efficient. Considering the power wall, hardware customization becomes more desirable. In this dissertation, we propose a hardware accelerator framework that is capable of handling irregular, vertex centric, and asynchronous graph applications. Developed high level SystemC models gives an abstraction to the programmer allowing to implement the hardware without extensive knowledge about the underlying architecture. With the given template, programmers are not limited to a single application since they can develop any graph application as long as it fits to the given template abstract. Besides the ability to develop different applications, the given template also decreases the time spent on developing and testing different accelerators. Additionally, an extensive experimental study shows that the proposed template can outperform a high-end 24 core CPU system up to 3x with up to 65x power efficiency.