An Adaptive Memory Interface Controller For Improving Bandwidth Utilization Of Hybrid And Reconfigurable Systems

Data mining, bioinformatics, knowledge discovery, social network analysis, are emerging irregular applications that exploits data structures based on pointers or linked lists, such as graphs, unbalanced trees or unstructured grids. These applications are characterized by unpredictable memory accesses and generally are memory bandwidth bound, but also presents large amounts of inherent dynamic parallelism because they can potentially spawn concurrent activities for each one of the element they are exploring. Hybrid architectures, which integrate general purpose processors with reconfigurable devices, appears promising target platforms for accelerating irregular applications. These systems often connect to distributed and multi-ported memories, potentially enabling parallel memory operations. However, these memory architectures introduce several challenges, such as the necessity to manage concurrency and synchronization to avoid structural conflicts on shared memory locations and to guarantee consistency. In this paper we present an adaptive Memory Interface Controller (MIC) that addresses these issues. The MIC is a general and customizable solution that can target several different memory structures, and is suitable for High Level Synthesis frameworks. It implements a dynamic arbitration scheme, which avoids conflicts on memory resources at runtime, and supports atomic memory operations, commonly exploited for synchronization directives in parallel programming paradigms. The MIC simultaneously maps multiple accesses to different memory ports, allowing fine grained parallelism exploitation and ensuring correctness also in the presence of irregular and statically unpredictable memory access patterns. We evaluated the effectiveness of our approach on a typical irregular kernel, graph Breadth First Search (BFS), exploring different design alternatives.