Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing Flow Completion Time via Adaptive Buffer Management in Data Center Networks

The traffic of modern data centers exhibits long-tail distribution, in which massive delay-sensitive short flows and a small number of bandwidth-hungry long flows co-exist. These two types of flows could share same bottleneck links in the data center networks but request different or even opposite network requirements. Existing solutions try to realize a trade-off between the requirements of different flows by either prioritizing short flows or limiting the buffer used by long flows at switches or end-hosts. However, they do not consider the dynamic traffic change and suffer from performance degradation, resulted from severe queueing delay and massive packet drops for short flows under current First-In-First- Out (FIFO) queueing mechanism. In this paper, we propose a novel buffer management scheme at switches, called Cut-in Queue (CQ), to achieve both low latency for short flows and high throughput for long flows. Based on network status in real time, CQ prioritizes short flows by dynamically cutting the short flows’ packets into the head of long flows or evicting some enqueued long flows’ packets and enables high throughput for long flows in most of the cases. Evaluation of both DPDK testbed and NS2 simulations show that CQ outperforms state-of-the-art buffer management schemes by reducing flow completion time by up to 73%.
Best VM Selection for Big Data Applications across Multiple Frameworks by Transfer Learning

Cloud providers are presented with a bewildering choice of VM types for a range of contemporary data processing frameworks today. However, existing performance modeling and machine learning efforts cannot pick optimal VM types for multiple frameworks simultaneously, since they are difficult to balance model accuracy and model training cost. We propose Vesta, a novel transfer learning approach, to address this challenge: (1) it abstracts knowledge of VM type selection through offline benchmarking on multiple frameworks; (2) it employs a two-layer bipartite graph to represent knowledge across frameworks; (3) it minimizes training overhead by reus-ing the knowledge to select the best VM type for given applications. Comparing with state-of-the-art efforts, our experiments on 30 applications of Hadoop, Hive and Spark show that Vesta can improve application performance up to 51% while reducing 85% training overhead.
A Graph Transformation System formalism for correctness of Transactional Memory algorithms

With the constant research and development of Transactional Memory (TM) systems, various algorithms have been proposed, and their correctness is always an important aspect to take into account. When analyzing TM algorithms, one of the most commonly used correctness criterion is opacity, which infers that the algorithm only generates executions that observe consistent states of the shared memory. In this work we present a formal definition to demonstrate that a given TM algorithm only generates opaque histories using a Graph Transformation System (GTS). We achieve that by introducing a methodology of translating an algorithm into production rules that manipulate the state of a graph. With a set initial state of transactions, the result of this method consists of a state space that includes every possible order of execution of transactional operations that are automatically checked for opacity, therefore all possible histories the algorithm can generate are checked for the safety property. As a case study we demonstrate our approach using the algorithm CaPR+.
DUB: Dynamic Underclocking and Bypassing in NoCs for Heterogeneous GPU Workloads

The performance of graphics processing units (GPU) workloads can be sensitive to the various clock domains which are dynamically tunable in modern GPUs. In this work, we observe that GPU application performance is sensitive towards NoC clock frequencies and the sensitivity varies during the execution of GPU kernels. We note that this heterogeneity is not adapted well by traditional dynamic voltage frequency scaling (DVFS) techniques. To that end, we introduce DUB, Dynamic Underclocking and Bypassing technique, for such heterogeneous GPU workloads. We enable bypassing retimer flops and routers while underclocking the NoC frequency thus enabling high power savings at minimal performance loss. Compared to baseline we observe a 26% improvement in power savings with only 3% degradation in performance beating oracular DVFS techniques.
Evaluating Optimizations for a High-Level Language

Pallene aims to be a system language counterpart for Lua, with similar syntax but ahead-of-time compilation. It also has optional typing and stricter semantics, allowing it to emit C code with unboxed values, which enable several optimizations in the C compiler. Assuming that, the current Pallene compiler does not implement most classic optimizations. However, the C compiler cannot perform all the expected optimizations due to the difference in the level of abstraction between Pallene and C. In this work, we studied several scenarios where the C compiler couldn’t perform those expected optimizations trying to understand why and implemented them in the Pallene compiler, which has higher-level understanding of the language. Among the implemented optimizations are function inlining and scalar replacement of aggregates, achieving results that bring evidence that higher level optimizations cannot be done in the lower level compiler.
SPMFS: A Scalable Persistent Memory File System on Optane Persistent Memory

The first commercial Non-Volatile Memory (NVM) (i.e., Intel Optane DC Persistent Memory) exhibits limited parallelism, especially for write operations, which is generally neglected by existing NVM-aware file systems. Besides, the concurrent control of file systems also limits their scalability on high-performance NVMs under mainstream multi-core architectures. To effectively exploit full parallelism inherent in both NVMs and multi-core processors to enhance the overall performance, this paper proposes a novel scalable persistent memory file system, called SPMFS. SPMFS first partitions global metadata structures of the file system into per-core structures to distribute load and relieve contention. Second, SPMFS presents a fine-grained range lock to support concurrent accesses upon a file. Finally, SPMFS designs a dedicated I/O thread pool to offer optimal parallelism inherent in underlying NVM regardless of varying user-threads. We implement an SPMFS prototype and evaluate it under a variety of workloads generated by IOtest, Filebench, FIO, and production traces from Alibaba Pangu. The experiments show that SPMFS provides better scalability, and achieves up to 2.37 × write throughput improvement over state-of-the-art kernel NVM-aware file systems (Ext4-DAX, NOVA, and PMFS) and user-space file systems (Strata and Libnvmmio), without sacrificing the read performance.
Analysis on the Risk of Commercial Banks in China during COVID-19

The rage of covid-19 violently rebuilt the order of human society, giving rise to potential recessions and deglobalization. In order to prevent gigantic financial institutions from ending up with insolvent situations, it is of vital significance to measure the impact of the pandemic on these firms. In this paper, the reliability of the top four commercial banks in China is painstakingly examined by utilizing linear regression and analysis of variance based on Stata. Thanks to Altman's Z-score model, the author manages to quantitatively measure the amount of credit risk the banks are currently taking. Based on the result of linear regression, this paper hardly finds supportive evidence on the hypothesis that the COVID has negative impact neither on banks’ financial performance nor on their risk management indicators. On the contrary, the outcome from ANOVA reveals that the Corona virus subtly raised the risk of commercial banks in China, especially for ABC and ICBC, whose liquidity dropped during 2020. Furthermore, the overall deteriorating trend in the industry is obvious but subtle, since these banks also raise their sales and profitability thanks to remote deals and commission fees. Taking advantage of Altman's Z-score, this paper estimates the impact of COVID-19 on the elite banks’ risk, reinforcing the importance of consummating the risk measure system as well as persisting in supervising the financial intermediates efficiently.
Analysis of on-chip communication properties in accelerator architectures for Deep Neural Networks

Deep neural networks (DNNs) algorithms are expected to be core components of next-generation applications. These high performance sensing and recognition algorithms are key enabling technologies of smarter systems that make appropriate decisions about their environment. The integration of these compute-intensive and memory-hungry algorithms into embedded systems will require the use of specific energy-efficient hardware accelerators. The intrinsic parallelism of DNNs algorithms allows for the use of a large number of small processing elements, and the tight exploitation of data reuse can significantly reduce power consumption. To meet these features, many dataflow models and on-chip communication proposals have been studied in recent years. This paper proposes a comprehensive study of on-chip communication properties based on the analysis of application-specific features, such as data reuse and communication models, as well as the results of mapping these applications to architectures of different sizes. In addition, the influence of mechanisms such as broadcast and multicast on performance and energy efficiency is analyzed. This study leads to the definition of overarching features to be integrated into next-generation on-chip communication infrastructures for CNN accelerators.
A Versatile Validation Framework for ERP and Oscillatory Brain Source Localization Using FieldTrip

The use of electroencephalography (EEG) continuously expanded during the last century leading this recording technique to be the standard in various domains. Considering its poor spatial resolution, many researchers have developed methods to reconstruct cortical source activations from scalp signals, but the lack of ground truth brain activity makes the reconstruction algorithms difficult to validate. To deal with this issue, simulated EEG data can be used to evaluate the reliability of the reconstructed sources. In the literature, benchmark frameworks are proposed to evaluate the reconstruction on fixed pseudo-EEG and other tools allow the user to generate custom data. However, to the best of our knowledge, none of the available pipelines proposes a uniform way to validate a reconstruction method from custom pseudo-data. We therefore present our versatile validation framework for EEG-based source localization. This new tool is an all-in-one validation pipeline aiming to evaluate source localization from pseudo-EEG data as close as possible to the experimental environment of the researcher. This paper presents the 5-step framework from the configuration to the evaluation and template data that can be used for benchmarking purposes. By using our uniform region-based evaluation method, researchers will be able to compare their reconstruction method on different configurations that fit their own experimental data. All the codes, template data and configuration description have been made available through https://github.com/numediart/ValidEEG.git.
Efficient Complete Event Trend Detection over High-Velocity Streams

Complete Event Trend (CET) detection over large-scale event streams is important and challenging in various applications such as financial services, real-time business analysis, and supply chain management. A potential large number of partial intermediate results during complex event matching can raise prohibitively high memory cost for the processing system. The state-of-the-art scheme leverages compact graph encoding, which represents the common sub-sequences of different complex events using a common sub-graph to achieve space efficiency for storing the intermediate results. However, we show that such a design raises unacceptable computation cost for the graph traversal needed whenever a new event comes. To address this problem, in this paper, we propose a novel attribute-based indexing (ABI) graph model to represent the relationship between events. By classifying the predicates and constructing the graph based on both the comparators in the predicates and the attribute values of the events, we achieve parallel event stream processing and efficient graph construction. Our design significantly reduces the total computation cost of graph construction from O(n2) to O(nlog(m)), where n is the number of events and m is the number of the attribute vertices. We further design several efficient traversal-based algorithms to extract CETs from the graph. We implement our design and conduct comprehensive experiments to evaluate the performance of this design. The results show that our design wins a couple of orders of magnitude back from state-of-the-art schemes.
Matryoshka: A Coalesced Delta Sequence Prefetcher

To learn complex memory access patterns effectively, many spatial data prefetchers have been proposed that characterize the patterns as fixed-length delta sequences. However, because complex patterns are variable in workloads, it is difficult for fixed-length delta sequences to recognize them with both high competitive coverage and accuracy. That is, longer delta sequences increase accuracy at a lower probability of pattern matching, while shorter delta sequences increase coverage at a higher probability of false predictions. A classical strategy is to introduce the multiple matching mechanism associated with variable-length delta sequences, but sequences have to be redundantly stored in multiple tables. We observe that shorter delta sequences can coalesce into longer sequences of a fixed length during learning processes. At the same time, the shorter ones can be extracted from the longer ones during matching processes. By leveraging this property, we can improve the storage efficiency of the multiple matching mechanism. In this paper, we propose a novel low-overhead prefetcher, named Matryoshka, that supports the multiple matching mechanism with high efficiency. Instead of maintaining variable-length delta sequences in multiple tables, Matryoshka coalesces variable-length delta sequences into fixed-length delta sequences, which can be maintained with a single pattern table. Concerning the evaluation of a simulated single-core system using memory-intensive workloads of SPEC 2017, Matryoshka outperforms the state-of-the-art prefetcher IPCP by 6.5% and surpasses SPP+PPF by 2.9% with 26 × lower storage overhead.
Distinct signatures of loss of consciousness during Focal Impaired Awareness versus Focal to Bilateral Tonic Clonic seizures

Loss of consciousness (LOC) is a hallmark of many epileptic seizures and carries risks of serious injury and sudden death. While cortical sleep-like activities accompany LOC during focal impaired awareness (FIA) seizures, the mechanisms of LOC during focal to bilateral tonic-clonic (FBTC) seizures remain unclear. Quantifying differences in markers of cortical activation and ictal recruitment between FIA and FBTC seizures may also help to understand their different consequences for clinical outcomes and to optimize neuromodulation therapies. We quantified clinical signs of LOC and intracranial EEG (iEEG) activity during 129 FIA and 50 FBTC from 41 patients. We characterized iEEG changes both in the seizure onset zone (SOZ) and in areas remote from SOZ with a total of 3386 electrodes distributed across brain areas. First, we compared the dynamics of iEEG sleep-like activities: slow-wave activity (SWA; 1-4 Hz) and beta/delta ratio (B/D; a validated marker of cortical activation) during FIA vs. FBTC. Second, we quantified differences between FBTC and FIA for a marker validated to detect ictal cross-frequency coupling: phase-locked high-gamma (PLHG; high gamma phased locked to low frequencies) and a marker of ictal recruitment: the epileptogenicity index (i.e. the number of channels crossing an energy ratio threshold for high vs. low frequency power). Third, we assessed changes in iEEG activity preceding and accompanying behavioral generalization onset and their correlation with electromyogram (EMG) channels. In addition, we analyzed human cortical multi-unit activity recorded with Utah arrays during three FBTC. Compared to FIA, FBTC seizures were characterized by deeper LOC and by stronger increases in SWA in parieto-occipital cortex. FBTC also displayed more widespread increases in cortical activation (B/D), ictal cross-frequency coupling (PLHG) and ictal recruitment (epileptogenicity index). Even before generalization, FBTC displayed deeper LOC; this early LOC was accompanied by a paradoxical increase in B/D in fronto-parietal cortex. Behavioral generalization coincided with complete loss of responsiveness and a subsequent increase in high-gamma in the whole brain, which was especially synchronous in deep sources and could not be explained by EMG. Similarly, multi-unit activity analysis of FBTC revealed sustained increases in cortical firing rates during and after generalization onset in areas remote from the SOZ. Unlike during FIA, LOC during FBTC is characterized by a paradoxical increase in cortical activation and neuronal firing. These findings suggest differences in the mechanisms of ictal LOC between FIA and FBTC and may account for the more negative prognostic consequences of FBTC.
It is the locus coeruleus! Or… is it?: a proposition for analyses and reporting standards for structural and functional magnetic resonance imaging of the noradrenergic locus coeruleus

The noradrenergic locus coeruleus (LC) in the brainstem shows early signs of protein pathologies in neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease. As the LC’s small size (approximately 2.5 mm in width) presents a challenge for molecular imaging, the past decade has seen a steep rise in structural and functional Magnetic Resonance (MR) studies aiming to characterise the LC’s changes in ageing and neurodegeneration. However, given its position in the brainstem and small volume, great care must be taken to yield methodologically reliable MR results as spatial deviations in transformations can greatly reduce the statistical power of the analyses at the group level. Here, we suggest a spatial transformation procedure and a set of quality assessment methods which allow LC researchers to achieve the spatial precision necessary for investigating this small but potentially impactful brain structure. Using a combination of available toolboxes (SPM12, ANTs, FSL, FreeSurfer), individual structural and functional 3T LC scans are transformed into MNI space via a study-specific anatomical template. Following this, the precision of spatial alignment in individual MNI-transformed images is quantified using in-plane distance measures based on slice-specific centroids of structural LC segmentations and based on landmarks of salient anatomical features in mean functional images, respectively. Median in-plane distance of all landmarks on the transformed structural as well as functional LC imaging data were below 2 mm, thereby falling below the typical LC width of 2.5 mm suggested by post-mortem data. With the set of spatial post-processing steps outlined in this paper and available for download, we hope to give readers interested in LC imaging a starting point for a reliable analysis of structural and functional MR data of the LC and to have also taken a first step towards establishing reporting standards of LC imaging data.
Machine learning-based modulation of Ca2+-binding affinity in EF-hand proteins and comparative structural insights into site-specific cooperative binding

Ca2+-binding proteins are present in almost all living organisms and different types display different levels of binding affinities for the cation. Here, we applied two new scoring schemes enabling the user to manipulate the binding affinities of such proteins. We specifically designed a unique EF-hand loop capable of binding calcium with high affinity by altering five residues of the loop based on the scoring scheme. We worked on the N-terminal domain of Entamoeba histolytica calcium-binding protein1 (NtEhCaBP1), and used site-directed mutagenesis to incorporate the designed loop sequence into the second EF hand motif of this protein. The binding isotherms calculated using ITC calorimetry showed a ∼500-fold greater association constant (Ka) for the mutant. The crystal structure of the mutant was also determined, and displayed more compact Ca2+-coordination spheres in both of its EF loops than did the structure of the wildtype protein, consistent with the greater calcium-binding affinities of the mutant. The NtEhCaBP1 mutant was also shown to form a hexamer rather than just a trimer, and this hexamer formation was attributed to the position of the last helix of the mutant having been changed as a result of the strong calcium coordination. Further dynamic correlation analysis revealed that the mutation in the second EF loop changed the entire residue network of the monomer, resulting in a stronger coordination of Ca2+ even in other EF hand loop.
Drosophila SUMM4 complex couples insulator function and DNA replication timing control

Asynchronous replication of chromosome domains during S phase is essential for eukaryotic genome function, but the mechanisms establishing which domains replicate early versus late in different cell types remain incompletely understood. Drosophila SNF2-related factor SUUR imparts under- replication of late-replicating intercalary heterochromatin in polytene chromosomes. SUUR negatively regulates DNA replication fork progression; however, its mechanism of action remains obscure. Here we developed a novel method termed MS-Enabled Rapid protein Complex Identification (MERCI) to isolate a stable stoichiometric native complex SUMM4 that comprises SUUR and a chromatin boundary protein Mod(Mdg4)-67.2. Mod(Mdg4) stimulates SUUR ATPase activity and is required for a normal spatiotemporal distribution of SUUR in vivo. SUUR and Mod(Mdg4)-67.2 together mediate the activities of gypsy insulator that prevent certain enhancer-promoter interactions and establish euchromatin-heterochromatin barriers in the genome. Furthermore, SuUR or mod(mdg4) mutations reverse under-replication of intercalary heterochromatin. Thus, SUMM4 can impart late replication of intercalary heterochromatin by attenuating the progression of replication forks through euchromatin/heterochromatin boundaries. Our findings reveal that DNA replication can be delayed by a chromatin barrier and uncover a critical role for architectural proteins in replication control. They suggest a mechanism for replication timing that does not depend on an asynchronous firing of replication origins.