DeepNVM++: Cross-Layer Modeling and Optimization Framework of Non-Volatile Memories for Deep Learning

Non-volatile memory (NVM) technologies such as spin-transfer torque magnetic random access memory (STT-MRAM) and spin-orbit torque magnetic random access memory (SOT-MRAM) have significant advantages compared to conventional SRAM due to their non-volatility, higher cell density, and scalability features. While previous work has investigated several architectural implications of NVM for generic applications, in this work we present DeepNVM++, a framework to characterize, model, and analyze NVM-based caches in GPU architectures for deep learning (DL) applications by combining technology-specific circuit-level models and the actual memory behavior of various DL workloads. We present both iso-capacity and iso-area performance and energy analysis for systems whose last-level caches rely on conventional SRAM and emerging STT-MRAM and SOT-MRAM technologies. In the iso-capacity case, STT-MRAM and SOT-MRAM provide up to 3.8x and 4.7x energy-delay product (EDP) reduction and 2.4x and 2.8x area reduction compared to conventional SRAM, respectively. Under iso-area assumptions, STT-MRAM and SOT-MRAM provide up to 2x and 2.3x EDP reduction and accommodate 2.3x and 3.3x cache capacity when compared to SRAM, respectively. We also perform a scalability analysis and show that STT-MRAM and SOT-MRAM achieve orders of magnitude EDP reduction when compared to SRAM for large cache capacities. Our comprehensive cross-layer framework is demonstrated on STT-/SOT-MRAM technologies and can be used for the characterization, modeling, and analysis of any NVM technology for last-level caches in GPUs for DL applications.Comment: 12 pages, 10 figure

On the Theory of Spatial and Temporal Locality

This paper studies the theory of caching and temporal and spatial locality. We show the following results: (1) hashing can be used to guarantee that caches with limited associativity behave as well as fully associative cache; (2) temporal locality cannot be characterized using one, or few parameters; (3) temporal locality and spatial locality cannot be studied separately; and (4) unlike temporal locality, spatial locality cannot be managed efficiently online

Catalog Dynamics: Impact of Content Publishing and Perishing on the Performance of a LRU Cache

The Internet heavily relies on Content Distribution Networks and transparent caches to cope with the ever-increasing traffic demand of users. Content, however, is essentially versatile: once published at a given time, its popularity vanishes over time. All requests for a given document are then concentrated between the publishing time and an effective perishing time. In this paper, we propose a new model for the arrival of content requests, which takes into account the dynamical nature of the content catalog. Based on two large traffic traces collected on the Orange network, we use the semi-experimental method and determine invariants of the content request process. This allows us to define a simple mathematical model for content requests; by extending the so-called "Che approximation", we then compute the performance of a LRU cache fed with such a request process, expressed by its hit ratio. We numerically validate the good accuracy of our model by comparison to trace-based simulation.Comment: 13 Pages, 9 figures. Full version of the article submitted to the ITC 2014 conference. Small corrections in the appendix from the previous versio

TaskInsight: Understanding Task Schedules Effects on Memory and Performance

Recent scheduling heuristics for task-based applications have managed to improve their by taking into account memory-related properties such as data locality and cache sharing. However, there is still a general lack of tools that can provide insights into why, and where, different schedulers improve memory behavior, and how this is related to the applications' performance. To address this, we present TaskInsight, a technique to characterize the memory behavior of different task schedulers through the analysis of data reuse between tasks. TaskInsight provides high-level, quantitative information that can be correlated with tasks' performance variation over time to understand data reuse through the caches due to scheduling choices. TaskInsight is useful to diagnose and identify which scheduling decisions affected performance, when were they taken, and why the performance changed, both in single and multi-threaded executions. We demonstrate how TaskInsight can diagnose examples where poor scheduling caused over 10% difference in performance for tasks of the same type, due to changes in the tasks' data reuse through the private and shared caches, in single and multi-threaded executions of the same application. This flexible insight is key for optimization in many contexts, including data locality, throughput, memory footprint or even energy efficiency.We thank the reviewers for their feedback. This work was supported by the Swedish Research Council, the Swedish Foundation for Strategic Research project FFL12-0051 and carried out within the Linnaeus Centre of Excellence UPMARC, Uppsala Programming for Multicore Architectures Research Center. This paper was also published with the support of the HiPEAC network that received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 687698.Peer ReviewedPostprint (published version