Random-LRU: A Replacement Policy for Chip Multiprocessors

Das, Shirshendu; Polavarapu, Nagaraju; Halwe, Prateek D.; Kapoor, Hemangee K.

doi:10.1007/978-3-642-42024-5_25

Cited by 8 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Random-LRU [5] proposes a cache replacement policy that combines Random and LRU policies. The entire cache is segmented into Random Partition (RP) and Replacement Partition (LP).…”

Section: Related Workmentioning

confidence: 99%

A hybrid cache replacement policy for heterogeneous multi-cores

AnandKumar¹,

S²,

Ganesh³

et al. 2014

2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

View full text Add to dashboard Cite

Future generation computer architectures are en deavoring to achieve high performance without compromise on energy efficiency. In a multiprocessor system, cache miss degrades the performance as the miss penalty scales by an exponential factor across a shared memory system when com pared to general purpose processors. This instigates the need for an efficient cache replacement scheme to cater to the data needs of underlying functional units in case of a cache miss.Minimal cache miss improves resource utilization and reduces data movement across the core which in turn contributes to a high performance and lesser power dissipation. Existing replacement policies has several issues when implemented in a heterogeneous multi-core system. The commonly used LRU replacement policy does not offer optimal performance for applications with high dependencies. Motivated by the limitations of the existing algorithms, we propose a hybrid cache replacement policy which combines Least Recently Used (LRU) and Least Frequently Used (LFU) replacement policies.Each cache block has two weighing values corresponding to LRU and LFU policies and a cumulative weight is calculated using these two values. Conducting simulations over wide range of cache sizes and associativity, we show that our proposed approach has shown increased cache hit to miss ratio when compared with LRU and other conventional cache replacement policies.

show abstract

“…Random-LRU [5] proposes a cache replacement policy that combines Random and LRU policies. The entire cache is segmented into Random Partition (RP) and Replacement Partition (LP).…”

Section: Related Workmentioning

confidence: 99%

A hybrid cache replacement policy for heterogeneous multi-cores

AnandKumar¹,

S²,

Ganesh³

et al. 2014

2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

View full text Add to dashboard Cite

show abstract

“…In both cases, a block occupies space unnecessarily until it reaches the LRU position. To avoid drawbacks of LRU, other primitive algorithms are proposed, namely MRU replacement policy and random replacement [6]. MRU replacement policy selects the most recently accessed block as victim with the prediction that the recently accessed block will not be accessed soon.…”

Section: Basic Replacement Policymentioning

confidence: 99%

A Categorical Study on Cache Replacement Policies for Hierarchical Cache Memory

Das

Roy

2020

Applications of Internet of Things

View full text Add to dashboard Cite

Cache memory plays an important role in the in-memory computation in memory-intensive applications. Hierarchical cache design is used to increase the capacity of cache to handle large working set. The last level cache (LLC) does not strictly follow the temporal locality of program, so it becomes challenging to identify the blocks that will not be reused (dead block). In this paper, we have performed a detail survey on different techniques to detect the dead blocks early in the cache memory and improve the hit rate of cache replacement algorithm. Belady's optimal solution detects the dead block by analyzing the future of blocks, which is completely un-realistic. Many researches have been done to detect dead block practically by observing the previous access pattern. Many algorithms are proposed to improve the performance of traditional replacement policies by considering different additional information. Most of the algorithm aims to reduce the miss count by retaining the blocks that will be reused before eviction (live blocks). Recent study observes that the cost of all the cache miss are not uniform in nature. So, some researchers have distinguished between high-cost block and low-cost block. The overall cost can be reduced by retaining the high-cost block in memory, with little higher miss count. It is observed that by managing cache miss un-coordinately among the different levels of cache memory, it is not possible to obtain maximum utilization of memory. Many adaptive algorithms have been proposed to maintain balance between the over-utilized blocks and underutilized blocks by the displacement of blocks. In this survey, we have categorized the practically implemented techniques into different classes based on their basic principle of cache replacement.

show abstract

“…Replacement policy is major area of research from the last two decades. The work in replacement policy can be divided into the following two categories: (a) Performance oriented like [7], [8], [9], [10] (b) Overhead reduction oriented like [11], [12]. The most efficient replacement policy was proposed in 1965 [13], which states that the evicted block must the block which will reuse in the furthest future.…”

Section: Introductionmentioning

confidence: 99%

“…Most of these techniques has high hardware overhead as they need to maintain some prediction tables. Some low-overhead based dead block predictors are [11], [12]. The technique proposed in this paper is also a low-overhead based replacement policy.…”

Section: Introductionmentioning

confidence: 99%

Small-LRU: A Hardware Efficient Hybrid Replacement Policy

Das¹,

Ranjan²

2020

IJACSA

View full text Add to dashboard Cite

Replacement policy plays a major role in improving the performance of the modern highly associative cache memories. As the demand of data intensive application is increasing it is highly required that the size of the Last Level Cache (LLC) must be increased. Increasing the size of the LLC also increases the associativity of the cache. Modern LLCs are divided into multiple banks where each bank is a set-associative cache. The replacement policy implemented on such highly associative banks consume significant hardware (storage and area) overhead. Also the Least Recently Used (LRU) based replacement policy has an issue of dead blocks. A block in the cache is called dead, if the block is not used in the future before its eviction from the cache. In LRU policy, a dead block can not be remove early until it become LRU-block. So, we have proposed a replacement technique which is capable of removing dead block early with reduced hardware cost between 77% to 91% in comparison to baseline techniques. In this policy random replacement is used for 70% ways and LRU is applied for rest of the ways. The early eviction of dead blocks also improves the performance of the system by 5%.

show abstract

Random-LRU: A Replacement Policy for Chip Multiprocessors

Cited by 8 publications

References 11 publications

A hybrid cache replacement policy for heterogeneous multi-cores

A hybrid cache replacement policy for heterogeneous multi-cores

A Categorical Study on Cache Replacement Policies for Hierarchical Cache Memory

Small-LRU: A Hardware Efficient Hybrid Replacement Policy

Contact Info

Product

Resources

About