Efficient Inter-Task Communication for Nested Loop Programs on a Multiprocessor System

In modern multiprocessor systems, processors can be stalled by inter-task communication when reading from a remote buffer. This paper presents a solution for the inter-task communication, that has a minimal impact on the performance of the system, hides the inter-task communication latency without requiring additional hardware. The solution applies to jobs, represented as task graphs, where the tasks are nested loop programs. Buffers are allocated in scratch-pad memories of the consuming tasks to provide low latency read access. For the nested loop programs, minimal buffer sizes can be determined to cover all possible communication patterns. The added computational complexity is low, as the solution adds only a few operations to the nested loop programs

Computation of Buffer Capacities for Throughput Constrained and Data Dependent Inter-Task Communication

Streaming applications are often implemented as task graphs. Currently, techniques exist to derive buffer capacities that guarantee satisfaction of a throughput constraint for task graphs in which the inter-task communication is data-independent, i.e. the amount of data produced and consumed is independent of the data values in the processed stream. This paper presents a technique to compute buffer capacities that satisfy a throughput constraint for task graphs with data dependent inter-task communication, given that the task graph is a chain. We demonstrate the applicability of the approach by computing buffer capacities for an MP3 playback application, of which the MP3 decoder has a variable consumption rate. We are not aware of alternative approaches to compute buffer capacities that guarantee satisfaction of the throughput constraint for this application

Buffer Capacity Computation for Throughput Constrained Streaming Applications with Data-Dependent Inter-Task Communication

Streaming applications are often implemented as task graphs, in which data is communicated from task to task over buffers. Currently, techniques exist to compute buffer capacities that guarantee satisfaction of the throughput constraint if the amount of data produced and consumed by the tasks is known at design-time. However, applications such as audio and video decoders have tasks that produce and consume an amount of data that depends on the decoded stream. This paper introduces a dataflow model that allows for data-dependent communication, together with an algorithm that computes buffer capacities that guarantee satisfaction of a throughput constraint. The applicability of this algorithm is demonstrated by computing buffer capacities for an H.263 video decoder

Applying Dataflow Analysis to Dimension Buffers for Guaranteed Performance in Networks on Chip

A Network on Chip (NoC) with end-to-end flow control is modelled by a cyclo-static dataflow graph. Using the proposed model together with state-of-the-art dataflow analysis algorithms, we size the buffers in the network interfaces. We show, for a range of NoC designs, that buffer sizes are determined with a run time comparable to existing analytical methods, and results comparable to exhaustive simulation