Cache capacity and its effects on power consumption for tiled chip multi-processors

Chakraborty, Shounak; Deb, Dipika; Buragohain, Dhantu; Kapoor, Hemangee K.

doi:10.1109/ecs.2014.6892719

Cited by 4 publications

(5 citation statements)

References 13 publications

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“…Address removal. Algorithm 1 presents a typical iterative framework for finding a minimal eviction set with O(|E| 2 ) complexity [33], where |E| denotes the cardinality of the initial set E. In each iteration, the attacker determines whether a candidate address e in the initial set E can be removed (lines [1][2][3][4][5][6][7][8][9][10][11]. Removing e should not make the remaining E insufficient for evicting x from the cache.…”

Section: B Minimal Eviction Setmentioning

confidence: 99%

“…The energy consumption of a cache is mainly composed of two parts: static power and dynamic power. Static power is generally referred as leakage power of the cache and dynamic power is consumed when the cache is accessed [7]. PhantomCache only affects the dynamic power of LLC because it only increases the cache activity during cache access.…”

Section: J Energy Overheadmentioning

confidence: 99%

See 1 more Smart Citation

PhantomCache: Obfuscating Cache Conflicts with Localized Randomization

Tan¹,

Zeng²,

et al. 2020

Proceedings 2020 Network and Distributed System Security Symposium

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Cache conflicts due to deterministic memory-tocache mapping have long been exploited to leak sensitive information such as secret keys. While randomized mapping is fully investigated for L1 caches, it still remains unresolved about how to secure a much larger last-level cache (LLC). Recent solutions periodically change the mapping strategy to disrupt the crafting of conflicted addresses, which is a critical attack procedure to exploit cache conflicts. Remapping, however, increases both miss rate and access latency. We present PhantomCache for securing an LLC with remapping-free randomized mapping. We propose a localized randomization technique to bound randomized mapping of a memory address within only a limited number of cache sets. The small randomization space offers fast set search over an LLC in a memory access. The intrinsic randomness still suffices to obfuscate conflicts and disrupt efficient exploitation of conflicted addresses. We evaluate PhantomCache against an attacker exploring the state-of-the-art attack with linear-complexity. To secure an 8-bank 16 MB 16-way LLC, PhantomCache confines randomization space of an address within 8 sets and brings only 1.20% performance degradation on individual benchmarks, 0.50% performance degradation on mixed workloads, and 0.50% storage overhead per cache line, which are 2x and 9x more efficient than the state-of-the-art solutions. Moreover, PhantomCache is solely an architectural solution and requires no software change. * Qinhan Tan and Zhihua Zeng have contributed equally and are considered to be co-first authors.

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Section: B Minimal Eviction Setmentioning

confidence: 99%

Section: J Energy Overheadmentioning

confidence: 99%

PhantomCache: Obfuscating Cache Conflicts with Localized Randomization

Tan¹,

Zeng²,

et al. 2020

Proceedings 2020 Network and Distributed System Security Symposium

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“…A computation interacts with memory in different ways as it moves through the levels of a memory hierarchy. Cache misses significantly influence power and energy consumption [31]. The cache, typically in SRAM technology, is known to be much more power efficient than DRAM-based main memory.…”

Section: Cache Miss Analysis and Other Memory Trendsmentioning

confidence: 99%

Investigation of Energy and Power Characteristics of Various Matrix Multiplication Algorithms

Alsari,

Al-Hashimi

2024

Energies

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This work studied the energy behavior of six matrix multiplication algorithms with various physical asset usage patterns. Two were variants of the straight inner product of rows and columns. The rest were variants of Strassen’s divide-and-conquer. Cases varied in ways that were expected to affect energy behavior. The study collected data for square matrix dimensions up to 4000. The research used reliable on-chip integrated voltage regulators embedded in a recent HPC-class AMD CPU for power measurements. Inner product methods used much less energy than the others for small to moderately large matrices. The advantage diminished for sufficiently large dimensions. The power draw of the inner product methods was less for small dimensions. After a point, the power advantage shifted significantly in favor of the divide-and-conquer group (average of 24% better), with the more block-optimized versions showing increased power efficiency (at least 8.3% better than the base method). The study explored the interplay between algorithm design, power efficiency, and computational resources. It aims to help advance the cause of power efficiency in HPC and other scenarios that rely on this vital computation.

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“…They have surveyed various cache tuning techniques based on two categories, offline static cache tuning and online dynamic cache tuning, ranging from hardware support to cache tuning techniques. In [46] the effect of power and performance are analyzed by reducing cache capacity. Reducing cache size in some applications give significant energy saving in order of 3.17% and 24.16% in dynamic and static energy respectively while degrade the performance by increasing cache misses.…”

Section: Microarchitectural Power Savingmentioning

confidence: 99%

Big Steps Towards Query Eco-Processing - Thinking Smart

Dembele

Bellatreche

Ordóñez

et al. 2021

Revue Africaine De Recherche en Informatique Et Mathématiques Appliquées

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Soumission à Episciences International audience Computers and electronic machines in businesses consume a significant amount of electricity, releasing carbon dioxide (CO2), which contributes to greenhouse gas emissions. Energy efficiency is a pressing concern in IT systems, ranging from mobile devices to large servers in data centers, in order to be more environmentally responsible. In order to meet the growing demands in the awareness of excessive energy consumption, many initiatives have been launched on energy efficiency for big data processing covering electronic components, software and applications. Query optimizers are one of the most power consuming components of a DBMS. They can be modified to take into account the energetical cost of query plans by using energy-based cost models with the aim of reducing the power consumption of computer systems. In this paper, we study, describe and evaluate the design of three energy cost models whose values of energy sensitive parameters are determined using the Nonlinear Regression and the Random Forests techniques. To this end, we study in depth the operating principle of the selected DBMS and present an analysis comparing the performance time and energy consumption of typical queries in the TPC benchmark. We perform extensive experiments on a physical testbed based on PostreSQL, MontetDB and Hyrise systems using workloads generatedusing our chosen benchmark to validate our proposal. Les ordinateurs et les machines électroniques des entreprises consomment une quantité importante d’électricité, libérant ainsi du dioxyde de carbone (CO2), qui contribue aux émissions de gaz à effet de serre. L’efficacité énergétique est une préoccupation urgente dans les systèmesinformatiques, partant des équipements mobiles aux grands serveurs dans les centres de données, afin d’être plus respectueux envers l’environnement. Afin de répondre aux exigences croissantes en matière de sensibilisation à l’utilisation excessive de l’énergie, de nombreuses initiatives ont été lancées sur l’efficacité énergétique pour le traitement des données massives couvrant les composantsélectroniques, les logiciels et les applications. Les optimiseurs de requêtes sont l’un des composants les plus énergivores d’un SGBD. Ils peuvent être modifiés pour prendre en compte le coût énergétique des plans des requêtes à l’aide des modèles de coût énergétiques intégrés dans l’optimiseur dans le but de réduire la consommation électrique des systèmes informatiques. Dans cet article, nousétudions, décrivons et évaluons la conception de trois modèles de coût énergétique dont les valeurs des paramètres sensibles à l’énergie sont définis en utilisant la technique de la Régression non linéaire et la technique des forêts aléatoires. Pour ce fait, nous menons une étude approfondie du principe de fonctionnement des SGBD choisis et présentons une analyse des performances en termes de temps et énergie sur des requêtes typiques du benchmarks TPC-H. Nous effectuons des expériences approfondies basées sur les systèmes PostgreSQL, MonetDB et Hyrise en utilisant un jeu de données généré à partir du benchmarks TPC-H afin de valider nos propositions.

show abstract

Cache capacity and its effects on power consumption for tiled chip multi-processors

Cited by 4 publications

References 13 publications

PhantomCache: Obfuscating Cache Conflicts with Localized Randomization

PhantomCache: Obfuscating Cache Conflicts with Localized Randomization

Investigation of Energy and Power Characteristics of Various Matrix Multiplication Algorithms

Big Steps Towards Query Eco-Processing - Thinking Smart

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