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1. Effective Mimicry of Belady’s MIN Policy

   https://doi.org/10.1109/HPCA53966.2022.00048  

   Shah, Ishan ; Jain, Akanksha ; Lin, Calvin ( April 2022 , International Symposium on High-Performance Computer Architecture)

   The past decade has seen the rise of highly successful cache replacement policies that are based on binary prediction. For example, the Hawkeye policy learns whether lines loaded by a given PC are Cache Friendly (likely to remain in the cache if Belady’s MIN policy had been used) or Cache Averse (likely to be evicted by Belady’s MIN policy). In this paper, we instead present a cache replacement policy that is based on multiclass prediction, which allows it to directly mimic Belady’s MIN policy in a surprisingly simple and effective way. Our policy uses a PC-based predictor to learn each cache line’s reuse distance; it then evicts lines based on their predicted time of reuse. We show that our use of multiclass prediction is more effective than binary prediction because it allows for a finer-grained ordering of cache lines during eviction and because it is more robust to prediction errors.Our empirical results show that our new policy, which we refer to as Mockingjay, outperforms the previous state-of-the-art on both single-core and multi-core platforms and both with and without a prefetcher. For example, with no prefetcher, on a mix of 100 multi-core workloads from the SPEC 2006, SPEC 2017, and GAP benchmark suites, Mockingjay sees an average improvement over LRU of 15.2%, compared to 7.6% for SHiP and 12.9% for Hawkeye. On a single-core platform, Mockingjay’s improvement over LRU is 5.7%, which approaches the 6.0% improvement of Belady MIN’s unrealizable policy. On a single-core platform (with a prefetcher) running the high-MPKI CVP workloads, Mockingjay’s improvement over LRU is 20.1%, compared to 13.4% for Hawkeye. 

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2. Multi-Tenant In-memory Key-Value Cache Partitioning Using Efficient Random Sampling-Based LRU Model

   https://doi.org/10.1109/TCC.2023.3300889  

   Wang, Yuchen ; Yang, Junyao ; Wang, Zhenlin ( August 2023 , IEEE Transactions on Cloud Computing)

   In-memory key-value caches are widely used as a performance-critical layer in web applications, disk-based storage, and distributed systems. The Least Recently Used (LRU) replacement policy has become the de facto standard in those systems since it exploits workload locality well. However, the LRU implementation can be costly due to the rigid data structure in maintaining object priority, as well as the locks for object order updating. Redis as one of the most effective and prevalent deployed commercial systems adopts an approximated LRU policy, where the least recently used item from a small, randomly sampled set of items is chosen to evict. This random sampling-based policy is lightweight and shows its flexibility. We observe that there can exist a significant miss ratio gap between exact LRU and random sampling-based LRU under different sampling size $K$ s. Therefore existing LRU miss ratio curve (MRC) construction techniques cannot be directly applied without loss of accuracy. In this paper, we introduce a new probabilistic stack algorithm named KRR to accurately model random sampling based-LRU, and extend it to handle both fixed and variable objects in key-value caches. We present an efficient stack update algorithm that reduces the expected running time of KRR significantly. To improve the performance of the in-memory multi-tenant key-value cache that utilizes random sampling-based replacement, we propose kRedis, a reference locality- and latency-aware memory partitioning scheme. kRedis guides the memory allocation among the tenants and dynamically customizes $K$ to better exploit the locality of each individual tenant. Evaluation results over diverse workloads show that our model generates accurate miss ratio curves for both fixed and variable object size workloads, and enables practical, low-overhead online MRC prediction. Equipped with KRR, kRedis delivers up to a 50.2% average access latency reduction, and up to a 262.8% throughput improvement compared to Redis. Furthermore, by comparing with pRedis, a state-of-the-art design of memory allocation in Redis, kRedis shows up to 24.8% and 61.8% improvements in average access latency and throughput, respectively. 

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3. A Deep Learning-Based Real-time Seizure Detection System

   https://doi.org/10.1109/SPMB50085.2020.9353623  

   Shawki, N. ; Elseify, T. ; Cap, T. ; Shah, V. ; Obeid, I. ; Picone, J. ( December 2020 , Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, Iyad Selesnick (Ed.)

   Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy [1]. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor [4] and Persyst 14 [5]. In this abstract, we describe our efforts to transform a high-performance offline seizure detection system [3] into a low latency real-time or online seizure detection system. An overview of the system is shown in Figure 1. The main difference between an online versus offline system is that an online system should always be causal and has minimum latency which is often defined by domain experts. The offline system, shown in Figure 2, uses two phases of deep learning models with postprocessing [3]. The channel-based long short term memory (LSTM) model (Phase 1 or P1) processes linear frequency cepstral coefficients (LFCC) [6] features from each EEG channel separately. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. Finally, Phase 3 aggregates the results from both P1 and P2 before applying a final postprocessing step. The online system implements Phase 1 by taking advantage of the Linux piping mechanism, multithreading techniques, and multi-core processors. To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. The feature extractor generates LFCC features in real time from the streaming EEG signal. Next, the system computes seizure and background probabilities using a channel-based LSTM model and applies a postprocessor to aggregate the detected events across channels. The system then displays the EEG signal and the decisions simultaneously using a visualization module. The online system uses C++, Python, TensorFlow, and PyQtGraph in its implementation. The online system accepts streamed EEG data sampled at 250 Hz as input. The system begins processing the EEG signal by applying a TCP montage [8]. Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Feature extraction is performed sequentially on each channel. The signal preprocessor writes the sample frames into two streams to facilitate these modules. In the first stream, the feature extractor receives the signals using stdin. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. This user-defined file holds raw signal information as a buffer for the visualizer. The signal preprocessor writes into the file while the visualizer reads from it. Reading and writing into the same file poses a challenge. The visualizer can start reading while the signal preprocessor is writing into it. To resolve this issue, we utilize a file locking mechanism in the signal preprocessor and visualizer. Each of the processes temporarily locks the file, performs its operation, releases the lock, and tries to obtain the lock after a waiting period. The file locking mechanism ensures that only one process can access the file by prohibiting other processes from reading or writing while one process is modifying the file [9]. The feature extractor uses circular buffers to save 0.3 seconds or 75 samples from each channel for extracting 0.2-second or 50-sample long center-aligned windows. The module generates 8 absolute LFCC features where the zeroth cepstral coefficient is replaced by a temporal domain energy term. For extracting the rest of the features, three pipelines are used. The differential energy feature is calculated in a 0.9-second absolute feature window with a frame size of 0.1 seconds. The difference between the maximum and minimum temporal energy terms is calculated in this range. Then, the first derivative or the delta features are calculated using another 0.9-second window. Finally, the second derivative or delta-delta features are calculated using a 0.3-second window [6]. The differential energy for the delta-delta features is not included. In total, we extract 26 features from the raw sample windows which add 1.1 seconds of delay to the system. We used the Temple University Hospital Seizure Database (TUSZ) v1.2.1 for developing the online system [10]. The statistics for this dataset are shown in Table 1. A channel-based LSTM model was trained using the features derived from the train set using the online feature extractor module. A window-based normalization technique was applied to those features. In the offline model, we scale features by normalizing using the maximum absolute value of a channel [11] before applying a sliding window approach. Since the online system has access to a limited amount of data, we normalize based on the observed window. The model uses the feature vectors with a frame size of 1 second and a window size of 7 seconds. We evaluated the model using the offline P1 postprocessor to determine the efficacy of the delayed features and the window-based normalization technique. As shown by the results of experiments 1 and 4 in Table 2, these changes give us a comparable performance to the offline model. The online event decoder module utilizes this trained model for computing probabilities for the seizure and background classes. These posteriors are then postprocessed to remove spurious detections. The online postprocessor receives and saves 8 seconds of class posteriors in a buffer for further processing. It applies multiple heuristic filters (e.g., probability threshold) to make an overall decision by combining events across the channels. These filters evaluate the average confidence, the duration of a seizure, and the channels where the seizures were observed. The postprocessor delivers the label and confidence to the visualizer. The visualizer starts to display the signal as soon as it gets access to the signal file, as shown in Figure 1 using the “Signal File” and “Visualizer” blocks. Once the visualizer receives the label and confidence for the latest epoch from the postprocessor, it overlays the decision and color codes that epoch. The visualizer uses red for seizure with the label SEIZ and green for the background class with the label BCKG. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. The user can plot the signal and decisions using the signal and HYP files with only the visualizer by enabling appropriate options. For comparing the performance of different stages of development, we used the test set of TUSZ v1.2.1 database. It contains 1015 EEG records of varying duration. The any-overlap performance [12] of the overall system shown in Figure 2 is 40.29% sensitivity with 5.77 FAs per 24 hours. For comparison, the previous state-of-the-art model developed on this database performed at 30.71% sensitivity with 6.77 FAs per 24 hours [3]. The individual performances of the deep learning phases are as follows: Phase 1’s (P1) performance is 39.46% sensitivity and 11.62 FAs per 24 hours, and Phase 2 detects seizures with 41.16% sensitivity and 11.69 FAs per 24 hours. We trained an LSTM model with the delayed features and the window-based normalization technique for developing the online system. Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. Available: https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor. [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” J. Clin. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, “Improved EEG Event Classification Using Differential Energy,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. https://doi.org/10.1109/SPMB.2015.7405421. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, “Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors,” Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9. 

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4. Analyzing the Benefits of More Complex Cache Replacement Policies in Moderns GPU LLCs

   Jarvis Jia ; Matthew D. Sinclair ( June 2023 , 5th gem5 Users' Workshop)

   The gem5 simulator offers Classic and Ruby as two separate memory models for simulating on-chip caches. The Classic model, which originated from M5, is a quick and simple option that allows for easy configuration, but only supports a basic MOESI coherence protocol. On the other hand, the Ruby model, which was developed by GEMS [2], is a more advanced and flexible option that can accurately simulate a wider range of cache coherence protocols and features. However, choosing between the two memory system models in gem5 is challenging for researchers as each has advantages and limitations which can be inconvenient. In particular, this has led to a bifurcation of effort where prior work has added replacement policies to Classic and Ruby in parallel – duplicating effort unnecessarily and preventing users from using a desired replacement policy if it is not implemented in the desired memory model (e.g., users could only use RRIP in Classic). Accordingly, we merged the cache replacement policies from Classic to Ruby, enabling users to use any of the replacement policies in either memory model. Gem5 currently has the capability to support 13 replacement policies, which can be used exchangeable within the Classic and Ruby cache models, including commonly used options like LRU, FIFO, PseudoLRU, and different types of RRIPs. After combining the replacement policies for the Classic and Ruby cache models, we designed and integrated (into gem5’s nightly regressions) multiple corner case tests to verify and ensure the continued correct functionality of these policies. Through these tests, we identified and fixed several bugs to ensure that the replacement policies operate correctly. Finally, with the newly enabled and verified functionality, since there is limited information about how different replacement policies affects GPU performance, we decided to use gem5 to study these policies in a GPU context. Specifically, we study GPU L2 caches, since GPU L1 caches are often used to stream data through and thus are unlikely to be significantly impacted by replacement policy. 

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5. Analyzing the Benefits of More Complex Cache Replacement Policies in Moderns GPU LLCs

   Jarvis Jia ; Matthew D. Sinclair ( June 2023 , 5th gem5 Users' Workshop)

   The gem5 simulator offers Classic and Ruby as two separate memory models for simulating on-chip caches. The Classic model, which originated from M5, is a quick and simple option that allows for easy configuration, but only supports a basic MOESI coherence protocol. On the other hand, the Ruby model, which was developed by GEMS [2], is a more advanced and flexible option that can accurately simulate a wider range of cache coherence protocols and features. However, choosing between the two memory system models in gem5 is challenging for researchers as each has advantages and limitations which can be inconvenient. In particular, this has led to a bifurcation of effort where prior work has added replacement policies to Classic and Ruby in parallel – duplicating effort unnecessarily and preventing users from using a desired replacement policy if it is not implemented in the desired memory model (e.g., users could only use RRIP in Classic). Accordingly, we merged the cache replacement policies from Classic to Ruby, enabling users to use any of the replacement policies in either memory model. Gem5 currently has the capability to support 13 replacement policies, which can be used exchangeable within the Classic and Ruby cache models, including commonly used options like LRU, FIFO, PseudoLRU, and different types of RRIPs. After combining the replacement policies for the Classic and Ruby cache models, we designed and integrated (into gem5’s nightly regressions) multiple corner case tests to verify and ensure the continued correct functionality of these policies. Through these tests, we identified and fixed several bugs to ensure that the replacement policies operate correctly. Finally, with the newly enabled and verified functionality, since there is limited information about how different replacement policies affects GPU performance, we decided to use gem5 to study these policies in a GPU context. Specifically, we study GPU L2 caches, since GPU L1 caches are often used to stream data through and thus are unlikely to be significantly impacted by replacement policy. 

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