Partitioning and allocation of scratch-pad memory for priority-based preemptive multi-task systems

Takase, Hideki; Tomiyama,; Takada,

doi:10.1109/date.2010.5456977

Cited by 30 publications

(40 citation statements)

References 12 publications

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“…MSRS may be regarded as an improvement of earlier approaches to prevent one task evicting local memory resources used by another, such as static cache locking [29] and static SPM partitioning [9], [7]. Simpler forms of MSRS have been proposed [30], [9], but the one used in this paper has the important distinction of enabling any task to use an arbitrary number of blocks in SPM independently of all other tasks [10].…”

Section: Related Workmentioning

confidence: 99%

Investigation of Scratchpad Memory for Preemptive Multitasking

Whitham

Davis

Audsley

et al. 2012

2012 IEEE 33rd Real-Time Systems Symposium

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Abstract-We present a multitasking scratchpad memory reuse scheme (MSRS) for the dynamic partitioning of scratchpad memory between tasks in a preemptive multitasking system. We specify a means to compute the worst-case response time (WCRT) and schedulability of task sets executed using MSRS. Our scratchpad-related preemption delay (SRPD) is an analog of cache-related preemption delay (CRPD), proposed in previous work as a way to compute the worst-case cost imposed upon a preempted task by preemption in a multitasking system. Unlike CRPD, however, SRPD is independent of the number of tasks and the local memory size.We compare SRPD with CRPD by experiment and determine that neither dominates the other, i.e. either may be better for certain task sets. However, MSRS leads to improved schedulability versus cache when contention for local memory space is high, either because the local memory size is small, or because the task set is large, provided that the cost of loading blocks from external memory to scratchpad is similar to the cost of loading blocks into cache.

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Section: Related Workmentioning

confidence: 99%

Investigation of Scratchpad Memory for Preemptive Multitasking

Whitham

Davis

Audsley

et al. 2012

2012 IEEE 33rd Real-Time Systems Symposium

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“…Spatial, temporal or hybrid methods have been introduced to bring about the effective utilization of SPM for Priority-Based Preemptive Multi-Task Systems [10]. It relocated the optimal task's functions to SPM, minimized the accesses to external MM.…”

Section: Related Workmentioning

confidence: 99%

“…The goal of the dynamic allocating scheme is to minimize the value of (9) with the constraint condition as (10).…”

Section: E(t S K ) Denotes the Total Energy Consumption Within T S Kmentioning

confidence: 99%

Dynamic Allocation of SPM Based on Time-Slotted Cache Conflict Graph for System Optimization

Ling

Zhang

et al. 2012

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper proposes a novel dynamic Scratch-pad Memory allocation strategy to optimize the energy consumption of the memory sub-system. Firstly, the whole program execution process is sliced into several time slots according to the temporal dimension; thereafter, a Time-Slotted Cache Conflict Graph (TSCCG) is introduced to model the behavior of Data Cache (D-Cache) conflicts within each time slot. Then, Integer Nonlinear Programming (INP) is implemented, which can avoid time-consuming linearization process, to select the most profitable data pages. Virtual Memory System (VMS) is adopted to remap those data pages, which will cause severe Cache conflicts within a time slot, to SPM. In order to minimize the swapping overhead of dynamic SPM allocation, a novel SPM controller with a tightly coupled DMA is introduced to issue the swapping operations without CPU's intervention. Last but not the least, this paper discusses the fluctuation of system energy profit based on different MMU page size as well as the Time Slot duration quantitatively. According to our design space exploration, the proposed method can optimize all of the data segments, including global data, heap and stack data in general, and reduce the total energy consumption by 27.28% on average, up to 55.22% with a marginal performance promotion. And comparing to the conventional static CCG (Cache Conflicts Graph), our approach can obtain 24.7% energy profit on average, up to 30.5% with a sight boost in performance.

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“…(2) The need for on-chip virtualization support for virtualized environments and onchip memory isolation to support trusted application execution. Figure 1 shows a set of applications being executed (App1-App4) by two CPUs (CPU0, CPU1) utilizing a total of two SPMs (4 KB space each) with temporal and spatial allocation (hybrid allocation) [38] and pre-defined schedules. Such schemes work well in closed systems, however, it might not be feasible to predict all the combinations of all the applications that will be running concurrently in an open system (e.g., Android).…”

Section: Motivationmentioning

confidence: 99%

“…Techniques for sharing the on-chip SPMs have been proposed [16,37,38], however, they assume that the applications are known ahead of time. These assumptions were true for closed systems, but as open systems (e.g., Android [17]) start to be widely adopted, what programs are loaded onto the device will not be necessarily known at compile time.…”

Section: Introductionmentioning

confidence: 99%

Virtualizing on-chip distributed ScratchPad memories for low power and trusted application execution

Bathen

Shin

Lim

et al. 2013

Des Autom Embed Syst

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Emerging multicore platforms are increasingly deploying distributed scratchpad memories to achieve lower energy and area together with higher predictability; but this requires transparent and efficient software management of these critical resources. In this paper, we introduce the concept of ScratchPad Memory virtualization, a hardware/software run-time layer (called SPMVisor) that virtualizes the scratchpad memory space in order to facilitate the use of distributed SPMs in an efficient, transparent and secure manner. We introduce the notion of virtual scratchpad memories (vSPMs), which can be dynamically created and managed as regular SPMs. The SPMVisor exploits policy-driven allocation strategies based on application privilege levels and data level prioritization metrics (e.g., utilization) to efficiently manage the on-chip memory real-estate. Our experimental results on Mediabench/CHStone benchmarks running on various Chip-Multiprocessor configurations and software stacks (RTOS, virtualization, secure execution) showed that SPMVisor enhances performance by 71 % on average and reduces power consumption by 79 % on average with respect to traditional context switching schemes. We showed the benefits of using vSPMs in a various environments (a RTOS multi-tasking environment, a virtualization environment, and a trusted execution environment). Furthermore, we explored the effects of mapping instructions and data onto vSPMs, and showed that sharing on-chip space reduces both execution time and energy by an average 16 % and 12 % respectively. We then compared our priority-driven memory allocation scheme with traditional dynamic allocation and showed an average 54 % execution time reduction and 65 % energy savings. Finally, to further validate the SPMVisor's benefits, we modified the initial bus-based architecture to include a mesh-based CMP with up to 4 × 4 nodes. We were able to observe that SPMVisor's priority-driven allocator was able to reduce execution time by an average 17 % with respect to competing allocation policies, while saving an average 65 % across various architectural configurations. We were also able to observe that SPMVisor reduces execution time by an average 12.6 % with respect to competing allocation policies, while saving an average 63.5 % in total energy for various architectural configuration running 1024 jobs.

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Partitioning and allocation of scratch-pad memory for priority-based preemptive multi-task systems

Cited by 30 publications

References 12 publications

Investigation of Scratchpad Memory for Preemptive Multitasking

Investigation of Scratchpad Memory for Preemptive Multitasking

Dynamic Allocation of SPM Based on Time-Slotted Cache Conflict Graph for System Optimization

Virtualizing on-chip distributed ScratchPad memories for low power and trusted application execution

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