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Performance-, power-, and energy-aware scheduling techniques play anessential role in optimally utilizing processing elements (PEs) ofheterogeneous systems. List schedulers, a class of low-complexity staticschedulers, have commonly been used in static execution scenarios. However,list schedulers are not suitable for runtime decision making, particularly whenmultiple concurrent applications are interleaved dynamically. For such cases,the static task execution times and expectation of idle PEs assumed by listschedulers lead to inefficient system utilization and poor performance. Toaddress this problem, we present techniques for optimizing execution of listscheduling algorithms in dynamic runtime scenarios via a family of algorithmsinspired by the well-known heterogeneous earliest finish time (HEFT) listscheduler. Through dynamically arriving, realistic workload scenarios that aresimulated in an open-source discrete event heterogeneous SoC simulator, weexhaustively evaluate each of the proposed algorithms across two SoCs modeledafter the Xilinx Zynq Ultrascale+ ZCU102 and O-Droid XU3 development boards.Altogether, depending on the chosen variant in this family of algorithms, weare able to achieve an up to 39% execution time improvement, up to 7.24xalgorithmic speedup, or up to 30% energy consumption improvement compared tothe baseline HEFT implementation.

Performant, Multi-objective Scheduling of Highly Interleaved Task Graphs on Heterogeneous System on Chip Devices