Technology trends present new challenges for processor architectures and their instruction schedulers. Growing transistor density will increase the number of execution units on a single chip, and decreasing wire transmission speeds will cause long and variable on-chip latencies. These trends will severely limit the two dominant conventional architectures: dynamic issue superscalars, and static placement and issue VLIWs. We present a new execution model in which the hardware and static scheduler instead work cooperatively, called Static Placement Dynamic Issue (SPDI). This paper focuses on the static instruction scheduler for SPDI. We identify and explore three issues SPDI schedulers must consider-locality, contention, and depth of speculation. We evaluate a range of SPDI scheduling algorithms executing on an Explicit Data Graph Execution (EDGE) architecture. We find that a surprisingly simple one achieves an average of 5.6 instructions-per-cycle (IPC) for SPEC2000 64-wide issue machine, and is within 80% of the performance without on-chip latencies. These results suggest that the compiler is effective at balancing on-chip latency and parallelism, and that the division of responsibilities between the compiler and the architecture is well suited to future systems.

Growing on-chip wire delays are motivating architectural features that expose on-chip communication to the compiler. EDGE architectures are one example of communication-exposed microarchitectures in which the compiler forms dataflow graphs that specify how the microarchitecture maps instructions onto a distributed execution substrate. This paper describes a compiler scheduling algorithm called spatial path scheduling that factors in previously fixed locations - called anchor points - for each placement. This algorithm extends easily to different spatial topologies. We augment this basic algorithm with three heuristics: (1) local and global ALU and network link contention modeling, (2) global critical path estimates, and (3) dependence chain path reservation. We use simulated annealing to explore possible performance improvements and to motivate the augmented heuristics and their weighting functions. We show that the spatial path scheduling algorithm augmented with these three heuristics achieves a 21% average performance improvement over the best prior algorithm and comes within an average of 5% of the annealed performance for our benchmarks.

Growing on-chip wire delays are motivating architectural features that expose on-chip communication to the compiler. EDGE architectures are one example of communication-exposed microarchitectures in which the compiler forms dataflow graphs that specify how the microarchitecture maps instructions onto a distributed execution substrate. This paper describes a compiler scheduling algorithm called spatial path scheduling that factors in previously fixed locations - called anchor points - for each placement. This algorithm extends easily to different spatial topologies. We augment this basic algorithm with three heuristics: (1) local and global ALU and network link contention modeling, (2) global critical path estimates, and (3) dependence chain path reservation. We use simulated annealing to explore possible performance improvements and to motivate the augmented heuristics and their weighting functions. We show that the spatial path scheduling algorithm augmented with these three heuristics achieves a 21% average performance improvement over the best prior algorithm and comes within an average of 5% of the annealed performance for our benchmarks.

Growing on-chip wire delays are motivating architectural features that expose on-chip communication to the compiler. EDGE architectures are one example of communication-exposed microarchitectures in which the compiler forms dataflow graphs that specify how the microarchitecture maps instructions onto a distributed execution substrate. This paper describes a compiler scheduling algorithm called spatial path scheduling that factors in previously fixed locations -called anchor points -for each placement. This algorithm extends easily to different spatial topologies. We augment this basic algorithm with three heuristics: (1) local and global ALU and network link contention modeling, (2) global critical path estimates, and (3) dependence chain path reservation. We use simulated annealing to explore possible performance improvements and to motivate the augmented heuristics and their weighting functions. We show that the spatial path scheduling algorithm augmented with these three heuristics achieves a 21% average performance improvement over the best prior algorithm and comes within an average of 5% of the annealed performance for our benchmarks.