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HePREM: A Predictable Execution Model for GPU-based Heterogeneous SoCs
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DOI：10.1109/TC.2020.2980520.png)
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Abstract
En 中文
The ever-increasing need for computational power in embedded devices has led to the adoption heterogeneous SoCs combining a general purpose CPU with a data parallel accelerator. These systems rely on a shared main memory (DRAM), which makes them highly susceptible to memory interference. A promising software technique to counter such effects is the Predictable Execution Model (PREM). PREM ensures robustness to interference by separating programs into a sequence of memory and compute phases, and by enforcing a platform-level schedule where only a single processing subsystem is permitted to execute a memory phase at a time. This article demonstrates for the first time how PREM can be applied to heterogeneous SoCs, based on a synchronization technique for memory isolation between CPU and GPU plus a compiler to transform GPU kernels into PREM-compliant codes. For compute bound GPU workloads sharing the DRAM bandwidth 50/50 with the CPU we guarantee near-zero timing varibility at a performance loss of just 59 percent, which is one to two orders of magnitude smaller than the worst case we see for unmodified programs under memory interference.
Keywords:
Graphics processing units
Central Processing Unit
Interference
Random access memory
Timing
Robustness
Memory management
Real-time and embedded systems
languages and compilers
graphics processors
memory management
reliability
runtime environments
parallel systems
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