Multi-core Digital Signal Processors (DSP) are widely used in wireless telecommunication, core network transcoding, industrial control, and audio/video processing etc. Comparing with general purpose multi-processors, the multi-core DSPs normally have more complex memory hierarchy, such as on-chip core-local memory and non-cache-coherent shared memory. As a result, it is very challenging to write efficient multi-core DSP applications. The current approach to program multi-core DSPs is based on proprietary vendor SDKs, which only provides low-level, non-portable primitives. While it is acceptable to write coarse-grained task level parallel code with these SDKs, it is very tedious and error prone to write fine-grained data parallel code with them. We believe it is desired to have a high-level and portable parallel programming model for multi-core DSPs. In this paper, we propose Open MDSP, an extension of Open MP designed for multi-core DSPs. The goal of Open MDSP is to fill the gap between Open MP memory model and the memory hierarchy of multi-core DSPs. We propose three class of directives in Open MDSP: (1) data placement directives allow programmers to control the placement of global variables conveniently, (2) distributed array directives divide whole array into sections and promote them into core-local memory to improve performance, and (3) stream access directives promote big array into core-local memory section by section during a parallel loop's processing. We implement the compiler and runtime system for Open MDSP on Free Scale MSC8156. Benchmarking result shows that seven out of nine benchmarks achieve a speedup of more than 5 with 6 threads.

Communication patterns of parallel applications are important to optimize application performance and design better communication subsystems. Communication patterns can be extracted from communication traces. However, existing approaches to generate communication traces need to execute the entire parallel applications on full-scale systems that are time consuming and expensive. We propose a novel technique, called Fact, which can perform FAst Communication Traces collection for large-scale parallel applications on small-scale systems. Our idea is to reduce the original program to obtain a program slice through static analysis, and to execute the program slice to acquire the communication traces. Our idea is based on an observation that most computation and message contents in parallel applications are not relevant to their spatial and volume communication attributes, and therefore can be removed for the purpose of communication trace collection. We have implemented Fact and evaluated it with NPB programs and Sweep3D. The results show that Fact can reduce resource consumptions by two orders of magnitude in most cases.

A proper understanding of communication patterns of parallel applications is important to optimize application performance and design better communication subsystems. Communication patterns can be obtained by analyzing communication traces. However, existing approaches to generate communication traces need to execute the entire parallel applications on full-scale systems that are time-consuming and expensive.