Browsing by Subject "High level synthesis"
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Item Open Access Accelerator design for graph analytics(Bilkent University, 2016-06) Yeşil, ŞerifWith 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.Item Open Access Energy efficient architecture for graph analytics accelerators(IEEE, 2016-06) Özdal, Muhammet Mustafa; Yeşil, Şerif; Kim, T.; Ayupov, A.; Greth, J.; Burns, S.; Öztürk, ÖzcanSpecialized hardware accelerators can significantly improve the performance and power efficiency of compute systems. In this paper, we focus on hardware accelerators for graph analytics applications and propose a configurable architecture template that is specifically optimized for iterative vertex-centric graph applications with irregular access patterns and asymmetric convergence. The proposed architecture addresses the limitations of the existing multi-core CPU and GPU architectures for these types of applications. The SystemC-based template we provide can be customized easily for different vertex-centric applications by inserting application-level data structures and functions. After that, a cycle-accurate simulator and RTL can be generated to model the target hardware accelerators. In our experiments, we study several graph-parallel applications, and show that the hardware accelerators generated by our template can outperform a 24 core high end server CPU system by up to 3x in terms of performance. We also estimate the area requirement and power consumption of these hardware accelerators through physical-aware logic synthesis, and show up to 65x better power consumption with significantly smaller area. © 2016 IEEE.Item Open Access A template-based design methodology for graph-parallel hardware accelerators(IEEE, 2017-05) Ayupov, A.; Yeşil, Şerif; Özdal, Muhammet Mustafa; Kim, T.; Burns, S.; Öztürk, ÖzcanGraph applications have been gaining importance in the last decade due to emerging big data analytics problems such as Web graphs, social networks, and biological networks. For these applications, traditional CPU and GPU architectures suffer in terms of performance and power consumption due to irregular communications, random memory accesses, and load balancing problems. It has been shown that specialized hardware accelerators can achieve much better power and energy efficiency compared to the general purpose CPUs and GPUs. In this paper, we present a template-based methodology specifically targeted for hardware accelerator design of big-data graph applications. Important architectural features that are key for energy efficient execution are implemented in a common template. The proposed template-based methodology is used to design hardware accelerators for different graph applications with little effort. Compared to an application-specific high-level synthesis methodology, we show that the proposed methodology can generate hardware accelerators with up to 18× better energy efficiency and requires less design effort.