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Precise, efficient, and context-sensitive cache analysis
Bounding the
Worst-Case Execution Time
(WCET) of real-time software requires precise knowledge about the reachable program and hardware states that might be observed at runtime. The analysis of precise cache states is particularly important and challenging. Due to the high cost of cache misses the analysis precision may have an important impact on the obtainable WCET bounds, while the large state space of the cache’s history leads to high analysis complexity. This work explores the use of
cache summaries
in order to optimize the computation of
precise
cache states. These cache summaries allow us to pre-compute the impact of executing a portion of a program, typically a function, on the cache state. This allows us, for instance, to
skip
the analysis of entire functions (including nested function calls) when the cache states within these functions are not relevant for the classification of memory accesses into hits/misses. Furthermore, the summaries can be extended to efficiently compute fully context-sensitive cache states. The summaries then not only allow to derive typical cache hit/miss classifications, but also provide fully context-sensitive cache persistence information.
Journal Article
Meow!
\"Can you find Stripy Cat's friends hiding under the flaps? Watch out-they might jump out at you!\"--Page 4 of cover.
CHILDBOOK
The Software Cache Optimization-Based Method for Decreasing Energy Consumption of Computational Clusters
Reducing the consumption of electricity by computing devices is currently an urgent task. Moreover, if earlier this problem belonged to the competence of hardware developers and the design of more cost-effective equipment, then more recently there has been an increased interest in this issue on the part of software developers. The issues of these studies are extensive. From energy efficiency issues of various programming languages to the development of energy-saving software for smartphones and other gadgets. However, to the best of our knowledge, no study has reported an analysis of the impact of cache optimizations on computing devices’ power consumption. Hence, this paper aims to provide an analysis of such impact on the software energy efficiency using the original software design procedure and computational experiments. The proposed Software Cache Optimization (SCO)-based Methodology was applied to one of the key linear algebra transformations. Experiments were carried out to determine software energy efficiency. RAPL (Running Average Power Limit) was used—an interface developed by Intel, which provides built-in counters of Central Processing Unit (CPU) energy consumption. Measurements have shown that optimized software versions reduce power consumption up to 4 times in relation to the basic transformation scheme. Experimental results confirm the effectiveness of the SCO-based Methodology used to reduce energy consumption and the applicability of this technique for software optimization.
Journal Article
Locality-protected cache allocation scheme with low overhead on GPUs
Graphics processing units (GPUs) are playing more important roles in parallel computing. Using their multi-threaded execution model, GPUs can accelerate many parallel programmes and save energy. In contrast to their strong computing power, GPUs have limited on-chip memory space which is easy to be inadequate. The throughput-oriented execution model in GPU introduces thousands of hardware threads, which may access the small cache simultaneously. This will cause cache thrashing and contention problems and limit GPU performance. Motivated by these issues, the authors put forward a locality-protected method based on instruction programme counter (LPC) to make use of data locality in L1 data cache with very low hardware overhead. First, they use a simple Program Counter (PC)-based locality detector to collect reuse information of each cache line. Then, a hardware-efficient prioritised cache allocation unit is proposed to coordinate data reuse information with time-stamp information to predict the reuse possibility of each cache line, and to evict the line with the least reuse possibility. Their experiment on the simulator shows that LPC provides an up to 17.8% speedup and an average of 5.0% improvement over the baseline method with very low overhead.
Journal Article
Adaptive template-based caching and LLM-driven summarization for richer student feedback insights
With increasing student enrollment in higher education, timely and effective analysis of student feedback has become essential for course evaluation and instructional improvement. Traditional methods often struggle with complex feedback containing mixed sentiments and lack the adaptability to accommodate evolving feedback entries. To address these limitations, we propose a novel framework that integrates Large Language Models (LLMs) with an adaptive template-based cache for comprehensive feedback analysis. By leveraging the semantic understanding capabilities of LLMs and a dynamic caching mechanism, our approach continuously updates and refines templates in response to new feedback themes. It employs hierarchical matching for fine-grained classification and uses In-Context Learning (ICL) to identify semantically relevant templates, thereby enhancing summary generation while minimizing redundancy. The Feedback Summary Generation component synthesizes sentiment trends and category-level distributions into actionable reports to support data-driven decisions in educational settings. Empirical results demonstrate the superiority of our framework in terms of summary quality, information coverage, sentiment classification, and processing efficiency, offering a robust and adaptable solution for feedback analysis across diverse educational environments.
Journal Article
ECAP: energy-efficient caching for prefetch blocks in tiled chip multiprocessors
With the increase in processing cores performance have increased, but energy consumption and memory access latency have become a crucial factor in determining system performance. In tiled chip multiprocessor, tiles are interconnected using a network and different application runs in different tiles. Non-uniform load distribution of applications results in varying L1 cache usage pattern. Application with larger memory footprint uses most of its L1 cache. Prefetching on top of such application may cause cache pollution by evicting useful demand blocks from the cache. This generates further cache misses which increases the network traffic. Therefore, an inefficient prefetch block placement strategy may result in generating more traffic that may increase congestion and power consumption in the network. This also dampens the packet movement rate which increases miss penalty at the cores thereby affecting Average Memory Access Time (AMAT). The authors propose an energy-efficient caching strategy for prefetch blocks, ECAP. It uses the less used cache set of nearby tiles running light applications as virtual cache memories for the tiles running high applications to place the prefetch blocks. ECAP reduces AMAT, router and link power in NoC by 23.54%, 14.42%, and 27%, respectively as compared to the conventional prefetch placement technique.
Journal Article
RCM: A Remote Cache Management Framework for Spark
With the rapid growth of Internet data, the performance of big data processing platforms is attracting more and more attention. In Spark, cache data are replaced by the Least Recently Used (LRU) Algorithm. LRU cannot identify the cost of cache data, which leads to replacing some important cache data. In addition, the placement of cache data is random, which lacks a measure to find efficient cache servers. Focusing on the above problems, a remote cache management framework (RCM) for the Spark platform was proposed, including a cache weight generation module (CWG), cache replacement module (CREP), and cache placement module (CPL). CWG establishes initial weights from three main factors: the response time of the query database, the number of queries, and the data size. Then, CWG reduces the old data weight through a time loss function. CREP promises that the sum of cache data weights is maximized by a greedy strategy. CPL allocates the best cache server for data based on the Kuhn-Munkres matching algorithm to improve cooperation efficiency. To verify the effectiveness of RCM, RCM is implemented on Redis and deployed on eight computing nodes and four cache servers. Three groups of benchmark jobs, PageRank, K-means and WordCount, is tested. The result of experiments confirmed that compared with MCM, SACM and DMAOM, the execution time of RCM is reduced by 42.1% at most.
Journal Article
DNS-Sensor: A Sensor-Driven Architecture for Real-Time DNS Cache Poisoning Detection and Mitigation
The Domain Name System (DNS) is a fundamental component of the Internet, yet its distributed and caching nature makes it susceptible to various attacks, especially cache poisoning. Although the use of random port numbers and transaction IDs has reduced the probability of cache poisoning, recent developments such as DNS Forwarder fragmentation and side-channel attacks have increased the possibility of cache poisoning. To counteract these emerging cache poisoning techniques, this paper proposes the DNS Cache Sensor (DNS-Sensor) system, which operates as a distributed sensor network for DNS security. Like environmental sensors monitoring physical parameters, DNS-Sensor continuously scans DNS cache records, comparing them with authoritative data to detect anomalies with sensor-grade precision. It involves checking whether the DNS cache is consistent with authoritative query results by continuous observation to determine whether cache poisoning has occurred. In the event of cache poisoning, the system switches to a disaster recovery resolution system. To expedite comparison and DNS query speeds and isolate the impact of cache poisoning on the disaster recovery resolution system, this paper uses a local top-level domain authoritative mirror query system. Experimental results demonstrate the accuracy of the DNS-Sensor system in detecting cache poisoning, while the local authoritative mirror query system significantly improves the efficiency of DNS-Sensor. Compared to traditional DNS, the integrated DNS query and DNS-Sensor method and local top-level domain authoritative mirror query system is faster, thus improving DNS performance and security.
Journal Article