Graph processing has been widely used in many scenarios, from scientific computing to artificial intelligence. Graph processing exhibits irregular computational parallelism and random memory accesses, unlike traditional workloads. Therefore, running graph processing workloads on conventional architectures (e.g., CPUs and GPUs) often shows a significantly low compute-memory ratio with few performance benefits, which can be, in many cases, even slower than a specialized single-thread graph algorithm. While domain-specific hardware designs are essential for graph processing, it is still challenging to transform the hardware capability to performance boost without coupled software codesigns. This article presents a graph processing ecosystem from hardware to software. We start by introducing a series of hardware accelerators as the foundation of this ecosystem. Subsequently, the codesigned parallel graph systems and their distributed techniques are presented to support graph applications. Finally, we introduce our efforts on novel graph applications and hardware architectures. Extensive results show that various graph applications can be efficiently accelerated in this graph processing ecosystem.

Many systems have been built to employ the delta-based iterative execution model to support iterative algorithms on distributed platforms by exploiting the sparse computational dependencies between data items of these iterative algorithms in a synchronous or asynchronous approach. However, for large-scale iterative algorithms, existing synchronous solutions suffer from slow convergence speed and load imbalance, because of the strict barrier between iterations; while existing asynchronous approaches induce excessive redundant communication and computation cost as a result of being barrier-free. In view of the performance trade-off between these two approaches, this paper designs an efficient execution manager, called Aiter-R, which can be integrated into existing delta-based iterative processing systems to efficiently support the execution of delta-based iterative algorithms, by using our proposed group-based iterative execution approach. It can efficiently and correctly explore the middle ground of the two extremes. A heuristic scheduling algorithm is further proposed to allow an iterative algorithm to adaptively choose its trade-off point so as to achieve the maximum efficiency. Experimental results show that Aiter-R strikes a good balance between the synchronous and asynchronous policies and outperforms state-of-the-art solutions. It reduces the execution time by up to 54.1% and 84.6% in comparison with existing asynchronous and the synchronous models, respectively.