Given the wide variety of performance demands for various workloads, the trend in embedded systems is shifting from homogeneous to heterogeneous processors, which have been shown to yield performance and energy saving benefits. A typical heterogeneous processor has cores with different performance and power characteristics, that is, high performance and power hungry ("big") cores, and low power and performance ("small") cores. In order to satisfy the memory bandwidth and computation demands of various threads, it is important (albeit challenging) to map threads to cores. Such assignment should take into account that threads could potentially be harmful to each other in the usage of shared resources (e.g., cache, memory).We propose a scheme for dynamic energy-efficient assignment of threads to big/small cores, DIO--E (Distributed Intensity Online-Energy), which is an enhancement of the previously proposed DIO. In contrast to DIO, we take into account both CPU and memory demands of threads to characterize the performance of threads when co-running on the same core at run-time. Our results show that DIO--E improves the energy-delay-squared product (ED2) by 9% (average) over DIO, running on a performance-asymmetric multicore system. Both DIO and DIO--E show about 50% improvement in ED2 over a state-of-the-art solution.

Energy-aware thread co-location in heterogeneous multicore processors

Single-ISA heterogeneous multi-core processors trade-off power with performance; however, threads that co-run on shared resources suffer from resource contention, which induces performance degradation and energy inefficiency. The authors introduce a novel approach to optimise the co-scheduling of multi-threaded applications on heterogeneous processors. The approach is based on the concept of stakes function, which represents the trade-off between isolation and sharing of resources. The authors also develop a co-scheduling algorithm that use stakes functions to optimise resource usage while mitigating resource contention, thus improving performance and energy efficiency. They validated the approach using applications from the Princeton Application Repository for Shared-Memory Computers (PARSEC) benchmark suite, obtaining up to 12.88% performance speed-up, 13.65% energy speed-up and 28.29% energy delay speed-up with respect to the standard Linux heterogeneous multi-processing scheduler.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-cdt.2015.0053

Co-scheduling tasks on multi-core heterogeneous systems: An energy-aware perspective

It is common that multiple cores reside on the same chip and share the on-chip cache. As a result, resource sharing can cause performance degradation of co-running jobs. Job co-scheduling is a technique that can effectively alleviate this contention and many co-schedulers have been reported in related literature. Most solutions however do not aim to find the optimal co-scheduling solution. Being able to determine the optimal solution is critical for evaluating co-scheduling systems. Moreover, most co-schedulers only consider serial jobs, and there often exist both parallel and serial jobs in real-world systems. In this paper a graph-based method is developed to find the optimal co-scheduling solution for serial jobs; the method is then extended to incorporate parallel jobs, including multi-process, and multi-threaded parallel jobs. A number of optimization measures are also developed to accelerate the solving process. Moreover, a flexible approximation technique is proposed to strike a balance between the solving speed and the solution quality. Extensive experiments are conducted to evaluate the effectiveness of the proposed co-scheduling algorithms. The results show that the proposed algorithms can find the optimal co-scheduling solution for both serial and parallel jobs. The proposed approximation technique is also shown to be flexible in the sense that we can control the solving speed by setting the requirement for the solution quality.

/pdf/developing-graph-based-co-scheduling-algorithms-on-multicore-3e0q7dpbzl.pdf

Developing Graph-Based Co-Scheduling Algorithms on Multicore Computers

It is common nowadays that multiple cores reside on the same chip and share the on-chip cache. Resource sharing may cause performance degradation of the co-running jobs. Job co-scheduling is a technique that can effectively alleviate the contention. Many co-schedulers have been developed in the literature, but most of them do not aim to find the optimal co-scheduling solution. Being able to determine the optimal solution is critical for evaluating co-scheduling systems. Moreover, most co-schedulers only consider serial jobs. However, there often exist both parallel and serial jobs in some situations. This paper aims to tackle these issues. In this paper, a graph-based method is developed to find the optimal co-scheduling solution for serial jobs, and then the method is extended to incorporate parallel jobs. The extensive experiments have been conducted to evaluate the effectiveness and efficiency of the proposed co-scheduling algorithms. The results show that the proposed algorithms can find the optimal co-scheduling solution for both serial and parallel jobs.

Optimizing Job Scheduling on Multicore Computers

Multicore applications contend for resources -- especially memory bandwidth -- reducing both quality-of-service and overall system performance. Contention-awareschedulers have been proposed to provide fairness and predictable behavior through thread-level scheduling. Prior approaches have two drawbacks, however. First, many introduce overhead that reduces overall performance. Second, the emergence of heterogeneous multicores has made handlingcontention and providing fairness much more difficult as thescheduler must now account for both application interferenceand the performance effects of different core types. This paper proposes augmenting existing contention-awareapproaches with predictive and adaptive components to providefair memory access and performance improvements on heterogeneous multicores. The predictive component's closed-loopapproach anticipates how different processes will perform withdifferent core types, while the adaptive component dynamicallytunes key scheduling parameters to the current workload. Weimplement and evaluate this approach on a real Linux/x86system with a variety of memory and compute intensivebenchmarks. We find that adding prediction improves fairnessand performance by 38% and 4% (respectively) compared to aprior state-of-the-art contention-aware approach. The additionof adaptation allows users to select for fairness or performanceoptimization, providing an additional 24% improvement infairness or a 9% improvement in performance beyond the predictive approach.

Providing Fairness in Heterogeneous Multicores with a Predictive, Adaptive Scheduler

Due to the limitation of power and processor complexity on traditional single core processors, multi-core processors have become the mainstream. One key feature on commodity multi-cores is that the last level cache (LLC) is usually shared. However, the shared cache contention can affect the performance of applications significantly. Several existing proposals demonstrate that task co-scheduling has the potential to alleviate the contention, but it is challenging to make co-scheduling practical in commodity operating systems. In this paper, we propose two lightweight practical textit{cache-aware} co-scheduling methods, namely static SOA and dynamic SOA, to solve the cache contention problem on commodity multi-cores. The central idea of the two methods is that textit{the cache contention can be reduced by adjusting the scheduling order properly}. These two methods are different from each other mainly in the way of acquiring the process's cache requirement. The static SOA (static scheduling order adjustment) method acquires the cache requirement information statically by offline profiling, while the dynamic SOA (dynamic scheduling order adjustment) captures the cache requirement statistics by using performance counters. Experimental results using multi-programmed NAS workloads suggest that the proposed methods can greatly reduce the effect of cache contention on multi-core systems. Specifically, for the static SOA method, the execution time can be reduced by up to 15.7%, the number of cache misses can be reduced by up to 11.8%, and the performance improvement remains obvious across the cache size and the length of time slice. For the dynamic SOA method, the execution time reduction can achieve up to 7.09%.

Haining Fan

Papers

Reducing Shared Cache Contention by Scheduling Order Adjustment on Commodity Multi-cores