A tuneable software cache coherence protocol for heterogeneous MPSoCs

ABSTRACT In a multiprocessor system-on-chip (MPSoC) private caches introduce the cache coherence problem. Here, we target at heterogeneous MPSoCs with a network-on-chip (NoC). Existing hardware cache coherence protocols are less suitable for MPSoCs because many off-the-shelf processors used in MPSoCs do not support these protocols. Furthermore, these protocols typically rely on global visibility and serialization of writes which does not match well with the parallel pointto-point communication provided by a NoC. Therefore, we propose a software cache coherence protocol, which can be applied in a heterogeneous MPSoC with a NoC. The software cache coherence protocol relies on explicit synchronization in the software. More specifically, caches are guaranteed to be coherent according to the Release Consistency model, on top of which we have implemented the standard Pthreads communication library. Heterogeneous MPSoCs with off-the-shelf processors can easily be supported, because processors are only required to provide cache control operations, e.g., clean and invalidate. All cache coherence operations are interruptible and do not impact the execution of tasks on other processors, therefore this protocol is suitable for predictable MPSoCs. Our software cache coherence protocol is implemented on an ARM926EJ-S MPSoC which is mapped on an FPGA. From experiments we conclude that the protocol overhead is low for the applications taken from the SPLASH-2 benchmark set. For these applications we observed a speedup between 1.89 and 2.01 on the two processor MPSoC

Programming models for many-core architectures: a co-design approach

Common many-core processors contain tens of cores and distributed memory. Compared to a multicore system, which only has a few tightly coupled cores sharing a single bus and memory, several complex problems arise. Notably, many cores require many parallel tasks to fully utilize the cores, and communication happens in a distributed and decentralized way. Therefore, programming such a processor requires the application to exhibit concurrency. In contrast to a single-core application, a concurrent application has to deal with memory state changes with an observable (non-deterministic) intermediate state. The complexity introduced by these problems makes programming a many-core system with a single-core-based programming approach notoriously hard.\ud \ud The central concept of this thesis is that abstractions, which are related to (many-core) programming, are structured in a single platform model. A platform is a layered view of the hardware, a memory model, a concurrency model, a model of computation, and compile-time and run-time tooling. Then, a programming model is a specific view on this platform, which is used by a programmer. In this view, some details can be hidden from the programmer's perspective, some details cannot. For example, an operating system presents an infinite number of parallel virtual execution units to the application whilst it hides details regarding scheduling. On the other hand, a programmer usually has balance workload among threads by hand.\ud \ud This thesis presents modifications to different abstraction layers of a many-core architecture, in order to make the system as a whole more efficient, and to reduce the programming complexity. These modifications influence other abstractions in the platform, and especially the programming model. Therefore, this thesis applies co-design on all models. Notably, co-design of the memory model, concurrency model, and model of computation is required for a scalable implementation of lambda-calculus. Moreover, only the combination of requirements of the many-core hardware from one side and the concurrency model from the other leads to a memory model abstraction. Hence, this thesis shows that to cope with the current trends in many-core architectures from a programming perspective, it is essential and feasible to inspect and adapt all abstractions collectively