Runtime Memory Allocation in a Heterogeneous Reconfigurable Platform

Abstract

In this paper, we present a runtime memory allocation algorithm, that aims to substantially reduce the overhead caused by shared-memory accesses by allocating memory directly in the local scratch pad memories. We target a heterogeneous platform, with a complex memory hierarchy. Using special instrumentation, we determine what memory areas are used in functions that could run on different processing elements, like, for example a reconfigurable logic array. Based on profile information, the programmer annotates some functions as candidates for accelerated execution. Then, an algorithm decides the best allocation, taking into account the various processing elements and special scratch pad memories of the the heterogeneous platform. Tests are performed on our prototype platform, a Virtex ML410 with Linux operating system, containing a PowerPC processor and a Xilinx FPGA, implementing the MOLEN programming paradigm. We test the algorithm using both state of the art H.264 video encoder as well as other synthetic applications. The performance improvement for the H.264 application is 14% compared to the software only version while the overhead is less than 1% of the application execution time. This improvement is the optimal improvement that can be obtained by optimizing the memory allocation. For the synthetic applications the results are within 5% of the optimum. 1

HArtes: Hardware-Software Codesign for Heterogeneous Multicore Platforms

“…More sophisticated code analysis techniques and corresponding optimizations are necessary. 7 Fifth, we must integrate advanced debugger facilities. A feature such as stepwise debugging of an FPGA-based kernel execution isn't a realistic option in the near future.…”