arXiv subjects

Alexandra Jimborean

Publications and source records attributed to Alexandra Jimborean.

3 recordsLinked to original sources

arXiv2026

PG-MDP: Profile-Guided Memory Dependence Prediction for Area-Constrained Cores

Memory Dependence Prediction (MDP) is a speculative technique to predict which stores, if any, a given load will depend on. Area-constrained cores are increasingly relevant in various applications such as energy-efficient or edge systems, and often have limited space for MDP tables. This leads to a high rate of false dependencies as memory independent loads alias with unrelated predictor entries, causing unnecessary stalls in the processor pipeline. The conventional way to address this problem is with greater predictor size or complexity, but this is unattractive on area-constrained cores. This paper demonstrates that targeting the predictor working set delivers the majority of available performance without scaling any hardware structures. We achieve this with profile-guided memory dependence prediction (PG-MDP), a hardware-software co-design to label consistently memory independent loads via their opcode and remove them from the MDP working set. These loads bypass querying the MDP and always issue as soon as possible. In the event that a labeled load incorrectly passes a store to the same address, a rollback is triggered as usual but no new MDP entry is created. Across the SPECspeed 2017 suites, PG-MDP reduces MDP load queries by 80%, false dependencies by 84%, and improves geomean IPC for a small (ROB=128) simulated core by 4.6% (to within 1.2% of the IPC when using 8x more predictor entries), with no area cost and no additional instruction bandwidth.

cs.PL↗

arXiv2017

Decoupled Access-Execute on ARM big.LITTLE

Energy-efficiency plays a significant role given the battery lifetime constraints in embedded systems and hand-held devices. In this work we target the ARM big.LITTLE, a heterogeneous platform that is dominant in the mobile and embedded market, which allows code to run transparently on different microarchitectures with individual energy and performance characteristics. It allows to se more energy efficient cores to conserve power during simple tasks and idle times and switch over to faster, more power hungry cores when performance is needed. This proposal explores the power-savings and the performance gains that can be achieved by utilizing the ARM big.LITTLE core in combination with Decoupled Access-Execute (DAE). DAE is a compiler technique that splits code regions into two distinct phases: a memory-bound Access phase and a compute-bound Execute phase. By scheduling the memory-bound phase on the LITTLE core, and the compute-bound phase on the big core, we conserve energy while caching data from main memory and perform computations at maximum performance. Our preliminary findings show that applying DAE on ARM big.LITTLE has potential. By prefetching data in Access we can achieve an IPC improvement of up to 37% in the Execute phase, and manage to shift more than half of the program runtime to the LITTLE core. We also provide insight into advantages and disadvantages of our approach, present preliminary results and discuss potential solutions to overcome locking overhead.

cs.DC↗

arXiv2016

Profiling-Assisted Decoupled Access-Execute

As energy efficiency became a critical factor in the embedded systems domain, dynamic voltage and frequency scaling (DVFS) techniques have emerged as means to control the system's power and energy efficiency. Additionally, due to the compact design, thermal issues become prominent. State of the art work promotes software decoupled access-execution (DAE) that statically generates code amenable to DVFS techniques. The compiler builds memory-bound access phases, designed to prefetch data in the cache at low frequency, and compute-bound phases, that consume the data and perform computations at high frequency. This work investigates techniques to find the optimal balance between lightweight and efficient access phases. A profiling step guides the selection of loads to be prefetched in the access phase. For applications whose behavior vary significantly with respect to the input data, the profiling can be performed online, accompanied by just-in-time compilation. We evaluated the benefits in energy efficiency and performance for both static and dynamic code generation and showed that precise prefetching of critical loads can result in 20% energy improvements, on average. DAE is particularly beneficial for embedded systems as by alternating access phases (executed at low frequency) and execute phases (at high frequency) DAE proactively reduces the temperature and therefore prevents thermal emergencies.

cs.AR↗