Miracle-dz/UniFAR-FTUs huggingface dataset:
ShakeFlow: Functional Hardware Description with Latency-Insensitive Interface Combinators

Functional programming’s benefits for hardware description have long been recognized in the literature. In particular, functional hardware description languages provide combinators such as maps and filters to facilitate the compositional description of circuits. However, it is challenging to apply functional programming with combinators to complex circuits with latency-insensitive interfaces such as valid/ready interfaces due to the cyclic nature of their forward and backward ports. In this work, we present ShakeFlow: the first functional hardware description language supporting latency-insensitive interface combinators. ShakeFlow provides extensible support for custom interfaces and combinators and a compiler to synthesizable Verilog and FIRRTL. We port a part of the BaseJump STL library and the Corundum 100Gbps NIC from (System)Verilog to ShakeFlow, reducing the code size by 38% and 26%, respectively. By experimenting with Corundum, we demonstrate that ShakeFlow is capable of designing realistic circuits, and porting to ShakeFlow does not incur significant resource and performance overhead.
Predicting Students' Performance Using Machine Learning Algorithms

Learning analytics (LA), defined as “the measurement, collection, analysis and reporting of data about learners and their contexts for the purposes of understanding and optimizing learning and the environments in which it occurs”, is a research topic that has gained significant importance and visibility among researchers during the past few decades. It is a research domain where the use of modern machine-learning (ML) algorithms and big data management provide timely and actionable information that can transform the overall learning experience for both students and educational institutions. In this paper we use ML algorithms in order to predict the performance of students, taking into account both past semester grades and socioeconomic factors. We run two models; a 2-class one predicting a “pass” or “fail” result and then we expanded this to a 5-class model, where we predict in which grading group the student will fall in the next semester. The results acquired indicate that it is possible to accurately predict the student's performance in both cases, with the 2-class model performing better than the 5-class one, which of course opts in providing more fine grain results.
Mobius: Fine Tuning Large-Scale Models on Commodity GPU Servers

Fine-tuning on cheap commodity GPU servers makes large-scale deep learning models benefit more people. However, the low inter-GPU communication bandwidth and pressing communication contention on the commodity GPU server obstruct training efficiency. In this paper, we present Mobius, a communication-efficient system for fine tuning large-scale models on commodity GPU servers. The key idea is a novel pipeline parallelism scheme enabling heterogeneous memory for large-scale model training, while bringing fewer communications than existing systems. Mobius partitions the model into stages and carefully schedules them between GPU memory and DRAM to overlap communication with computation. It formulates pipeline execution into a mixed-integer program problem to find the optimal pipeline partition. It also features a new stage-to-GPU mapping method termed cross mapping, to minimize communication contention. Experiments on various scale models and GPU topologies show that Mobius significantly reduces the training time by 3.8-5.1× compared with the prior art.
Using Database Schemas of Legacy Applications for Microservices Identification: A Mapping Study

The extraction of software components or services from a legacy system, is a very active field of research especially after the introduction of the microservices architectures. There are many different proposed approaches to solve this complex problem with the help of static and dynamic analysis of the source code, domain driven business dataflow diagrams etc. When we are dealing with a legacy system (e.g., a system with monolithic architecture) the most common asset that we inherit, aside from the application's code itself, is the database schema and its data. The source code isn't always available for analysis and the dataflow diagrams although helpful to some extent, they do not provide any reuse of the system's source code. The purpose of this research is to systematically identify primary studies that are using the database artefacts for microservice extraction, analyse the type of the artefacts and the degree they are used, and lastly investigate if the proposed methods support automation. We will then try to verbally analyse their viability in a real world scenario based on our many year experience working with legacy systems. In the context of this research, we conducted a systematic mapping study regarding the usage of database artefacts for the identification of components or services. This article presents the results of this study. Our future research goal is to use the database schema, state of data, dependencies, foreign keys etc., for identifying discrete business units within the legacy system database. Each such unit will have loose coupling with other units and be highly cohesive making it a potential microservice.
VDom: Fast and Unlimited Virtual Domains on Multiple Architectures

Hardware memory domain primitives, such as Intel MPK and ARM Memory Domain, have been used for efficient in-process memory isolation. However, they can only provide a limited number of memory domains (16 domains), which cannot satisfy the compelling need for more isolated domains inside the address space of a process. Existing solutions to virtualize memory domains are either intrusive (need the modification to existing hardware), or incur a large performance overhead. In this paper, we propose VDom, a fast and scalable memory domain virtualization system that supports unlimited memory domains. VDom leverages separate address spaces to provide an unlimited number of virtual domains, and optimizes related memory management operations. To map virtual domains to hardware domains, we design a domain virtualization algorithm, which manages address spaces and domain maps for threads to efficiently access other domains that are unmapped in the current address space. According to our evaluation on real Intel and ARM platforms, on real-world server applications (httpd and MySQL), VDom incurs less than 2.65% performance overhead, which is lower than the overheads of the state-of-the-art software approaches (libmpk and EPK). In random domain access tests, VDom is comparable to EPK and has significantly higher efficiency than libmpk.
Propeller: A Profile Guided, Relinking Optimizer for Warehouse-Scale Applications

While profile guided optimizations (PGO) and link time optimiza-tions (LTO) have been widely adopted, post link optimizations (PLO)have languished until recently when researchers demonstrated that late injection of profiles can yield significant performance improvements. However, the disassembly-driven, monolithic design of post link optimizers face scaling challenges with large binaries andis at odds with distributed build systems. To reconcile and enable post link optimizations within a distributed build environment, we propose Propeller, a relinking optimizer for warehouse scale work-loads. To enable flexible code layout optimizations, we introduce basic block sections, a novel linker abstraction. Propeller uses basic block sections to enable a new approach to PLO without disassembly. Propeller achieves scalability by relinking the binary using precise profiles instead of rewriting the binary. The overhead of relinking is lowered by caching and leveraging distributed compiler actions during code generation. Propeller has been deployed to production at Google with over tens of millions of cores executing Propeller optimized code at any time. An evaluation of internal warehouse-scale applications show Propeller improves performance by 1.1% to 8% beyond PGO and ThinLTO. Compiler tools such as Clang improve by 7% while MySQL improves by 1%. Compared to the state of the art binary optimizer, Propeller achieves comparable performance while lowering memory overheads by 30%-70% on large benchmarks.
Revisiting Log-Structured Merging for KV Stores in Hybrid Memory Systems

We present MioDB, a novel LSM-tree based key-value (KV) store system designed to fully exploit the advantages of byte-addressable non-volatile memories (NVMs). Our experimental studies reveal that the performance bottleneck of LSM-tree based KV stores using NVMs mainly stems from (1) costly data serialization/deserialization across memory and storage, and (2) unbalanced speed between memory-to-disk data flushing and on-disk data compaction. They may cause unpredictable performance degradation due to write stalls and write amplification. To address these problems, we advocate byte-addressable and persistent skip lists to replace the on-disk data structure of LSM-tree, and design four novel techniques to make the best use of fast NVMs. First, we propose one-piece flushing to minimize the cost of data serialization from DRAM to NVM. Second, we exploit an elastic NVM buffer with multiple levels and zero-copy compaction to eliminate write stalls and reduce write amplification. Third, we propose parallel compaction to orchestrate data flushing and compactions across all levels of LSM-trees. Finally, MioDB increases the depth of LSM-tree and exploits bloom filters to improve the read performance. Our extensive experimental studies demonstrate that MioDB achieves 17.1× and 21.7× lower 99.9th percentile latency, 8.3× and 2.5× higher random write throughput, and up to 5× and 4.9× lower write amplification compared with the state-of-the-art NoveLSM and MatrixKV, respectively.
Ditto: End-to-End Application Cloning for Networked Cloud Services

The lack of representative, publicly-available cloud services has been a recurring problem in the architecture and systems communities. While open-source benchmarks exist, they do not capture the full complexity of cloud services. Application cloning is a promising way to address this, however, prior work is limited to CPU-/cache-centric, single-node services, operating at user level. We present Ditto, an automated framework for cloning end-to-end cloud applications, both monolithic and microservices, which captures I/O and network activity, as well as kernel operations, in addition to application logic. Ditto takes a hierarchical approach to application cloning, starting with capturing the dependency graph across distributed services, to recreating each tier's control/data flow, and finally generating system calls and assembly that mimics the individual applications. Ditto does not reveal the logic of the original application, facilitating publicly sharing clones of production services with hardware vendors, cloud providers, and the research community. We show that across a diverse set of single- and multi-tier applications, Ditto accurately captures their CPU and memory characteristics as well as their high-level performance metrics, is portable across platforms, and facilitates a wide range of system studies.
Tridigpu: A GPU Library for Block Tridiagonal and Banded Linear Equation Systems

In this article, we present a CUDA library with a C API for solving block cyclic tridiagonal and banded systems on one GPU. The library can process block tridiagonal systems with block sizes from 1 × 1 (scalar) to 4 × 4 and banded systems with up to four sub- and superdiagonals. For the compute-intensive block size cases and cases with many right-hand sides, we write out an explicit factorization to memory; however, for the scalar case, the fastest approach is to only output the coarse system and recompute the factorization. Prominent features of the library are (scaled) partial pivoting for improved numeric stability; highest-performance kernels, which completely utilize GPU memory bandwidth; and support for multiple sparse or dense right-hand side and solution vectors. The additional memory consumption is only 5% of the original tridiagonal system, which enables the solution of systems up to GPU memory size. The performance of the state-of-the-art scalar tridiagonal solver of cuSPARSE is outperformed by factor 5 for large problem sizes of 225 unknowns, on a GeForce RTX 2080 Ti.
MSCCLang: Microsoft Collective Communication Language

Machine learning models with millions or billions of parameters are increasingly trained and served on large multi-GPU systems. As models grow in size and execute on more GPUs, collective communication becomes a bottleneck. Custom collective algorithms optimized for both particular network topologies and application-specific communication patterns can alleviate this bottleneck and help these applications scale. However, implementing correct and efficient custom algorithms is challenging. This paper introduces MSCCLang, a system for programmable GPU communication. MSCCLang provides a domain specific language for writing collective communication algorithms and an optimizing compiler for lowering them to an executable form, which can be executed efficiently and flexibly in an interpreter-based runtime. We used MSCCLang to write novel collective algorithms for AllReduce and AllToAll that are up to 1.9× and 1.3× faster than hand-optimized implementations, respectively.
Impact of Awash irrigation on the welfare of smallholder farmers in Eastern Ethiopia

Abstract Ethiopia’s agriculture is dominated by small-scale rain-fed production in combinations of natural and manmade factors have resulted in serious poverty. Irrigation farming is increasing been used as a strategy in Ethiopia. However, lack of consensus on the role of the irrigation sector on the welfare of smallholder farmers and pitfalls in impact study methodologies resulted in mixed findings. This study evaluated the impact of Awash irrigation on the welfare of rural smallholder farmers. Two-stage stratified sampling technique employed to select sample households. Cross-sectional household level data from a survey of 315; 165 irrigation users and 151 non-users smallholder farmers in Asiyta district, Ethiopia used for the analysis. This study employed endogenous switching regression model to control for endogeneity problems associated with adoption decision. Accordingly, the correlation coefficient result proved that the existence of self-selection and endogeneity. Results indicated, irrigation users’ per capita consumption expenditure and income were 16 percent and 35 percent, respectively, higher compared to non-irrigation-users significantly. Endogenous switching regression model further identified amount of own land cultivated, education status, number of extension contact, livestock holding, nearest market distance, access to non-farm job and nearest canal distance significantly determine irrigation participation. The study concluded that Awash irrigation is one of the viable solutions to improve the welfare of smallholder farmers in the study area. Therefore, governmental and non-governmental organization should promote, improve and expand Awash irrigation in all areas of the Woreda in particular and irrigation agriculture in general. Figure 1. Conceptual framework for determinants of irrigation participation.Source: Modified from Mengistie and Kidane (2016)
Modifications of the steady-state free-precession sequence for the detection of pure nuclear quadrupole resonance

Modifications of the steady-state free-precession (SSFP) sequence are proposed for the effective detection of pure nuclear quadrupole resonance (NQR). These modifications enable the suppression of the effect of intensity anomalies observed under steady-state conditions. The steady-state signals in SSFP-type sequences strongly depend on the frequency offset from resonance and the pulse spacing. This dependence has some peculiarities which were taken into consideration. Experimental results of modified SSFP sequence applications are reported for nitrogen-14 NQR in a plastic explosive.
The LCLAT1/LYCAT acyltransferase is required for EGF-mediated phosphatidylinositol-3,4,5-trisphosphate and Akt signalling

Receptor tyrosine kinases such as epidermal growth factor receptor (EGFR) stimulate phosphoinositide 3-kinases (PI3Ks) to convert phosphatidylinositol-4,5-bisphosophate [PtdIns(4,5)P2] into phosphatidylinositol-3,4,5-trisphosphate [PtdIns(3,4,5)P3]. PtdIns(3,4,5)P3 then remodels actin and gene expression patterns, and boosts cell survival and proliferation. PtdIns(3,4,5)P3 partly achieves these functions by stimulating the kinase Akt, which phosphorylates targets like Tsc2 and GSK3β. Consequently, unchecked upregulation of PtdIns(3,4,5)P3-Akt signalling promotes tumourgenesis. Interestingly, 50-70% of PtdIns and PtdInsPs have stearate and arachidonate at sn-1 and sn-2 positions of glycerol, respectively, forming a species known as 38:4-PtdIns/PtdInsPs. LCLAT1 and MBOAT7 acyltransferases are respectively responsible for enriching PtdIns in this acyl format. We previously showed that disruption of LCLAT1 lowered PtdIns(4,5)P2 levels and perturbed endocytosis and endocytic trafficking. However, the role of LCLAT1 in receptor tyrosine kinase and PtdIns(3,4,5)P3 signaling was not explored. Here, we show that LCLAT1 silencing in MDA-MB-231 and ARPE-19 cells abated the levels of PtdIns(3,4,5)P3 in response to EGF signalling. Importantly, LCLAT1-silenced cells were also impaired for EGF-driven Akt activation and downstream signalling, and consequently, were depressed for cell proliferation and survival. Thus, our work provides first evidence that the LCLAT1 acyltransferase is required for receptor tyrosine kinase signalling and may represent a novel target for therapeutic development against cancers.
PP1β opposes classic PP1 function, inhibiting spine maturation and promoting LTP

Protein phosphatase 1 (PP1) regulates synaptic plasticity and has been described as a molecular constraint on learning and memory. There are three neuronal isoforms, PP1α, PP1β, and PP1γ, but little is known about their individual functions. PP1α and PP1γ are assumed to mediate the effects of PP1 on learning and memory based on their enrichment at dendritic spines and their preferential binding to neurabin and spinophilin, major PP1 synaptic scaffolding proteins. However, it was recently discovered that human de novo PP1β mutations cause intellectual disability, suggesting an important but ill-defined role for PP1β. In this study, we investigated the functions of each PP1 isoform in hippocampal synaptic physiology using conditional CA1-specific knockout mice. In stark contrast to classic PP1 function, we found that PP1β promotes synaptic plasticity as well as spatial memory. These changes in synaptic plasticity and memory are accompanied by changes in GluA1 phosphorylation, GluN2A levels, and dendritic spine density and morphology, including silent synapse number. These functions of PP1β reveal a previously unidentified signaling pathway regulating spine maturation and plasticity, broadening our understanding of the complex role of PP1 in synaptic physiology.
Functional optimization of light-activatable Opto-GPCRs: Illuminating the importance of the proximal C-terminus in G-protein specificity

G-protein coupled receptors (GPCRs) are the largest family of human receptors that transmit signals from natural ligands and pharmaceutical drugs into essentially every physiological process. One main characteristic of GPCRs is their ability to specifically couple with different families of G-proteins, thereby triggering specific downstream signaling pathways. While an abundance of structural information is available on GPCR interactions with G-proteins, little is known about the GPCR domains functionally mediating G-protein specificity, in particular the proximal C-terminus, the structure which cannot be predicted with high confidentiality due to its flexibility. In this study, we exploited OptoGPCR chimeras between light-gated GPCRs (opsins) and ligand-gated GCPRs to systematically investigate the involvement of the C-terminus steering G-protein specificity. We employed rhodopsin-beta2-adrenoceptor and melanopsin-mGluR6 chimeras. We discovered a dominant role of the proximal C-terminus, dictating G-protein selectivity in the melanopsin-mGluR6 chimera, whereas it is the intracellular loop 3, which steers G-protein tropism in the rhodopsin-beta2-adrenoceptor. From the functional results and structural predictions, melanopsin and mGluR6 use a different mechanism to bRhod and b2AR to couple to a selective G-protein. Collectively, this work adds knowledge to the GPCR domains mediating G-protein selectivity, ultimately paving the way to optogenetically elicited specific G-protein signaling on demand.
Deep learning enables fast, gentle STED microscopy

STED microscopy is widely used to image subcellular structures with super-resolution. Here, we report that denoising STED images with deep learning can mitigate photobleaching and photodamage by reducing the pixel dwell time by one or two orders of magnitude. Our method allows for efficient and robust restoration of noisy 2D and 3D STED images with multiple targets and facilitates long-term imaging of mitochondrial dynamics.
Blending Sea Surface Temperatures from Multiple Satellites and In Situ Observations for Coastal Oceans

Abstract A two-dimensional variational data assimilation (2DVAR) method for blending sea surface temperature (SST) data from multiple observing platforms is presented. This method produces continuous fields and has the capability of blending multiple satellite and in situ observations. In addition, it allows specification of inhomogeneous and anisotropic background correlations, which are common features of coastal ocean flows. High-resolution (6 km in space and 6 h in time) blended SST fields for August 2003 are produced for a region off the California coast to demonstrate and evaluate the methodology. A comparison of these fields with independent observations showed root-mean-square errors of less than 1°C, comparable to the errors in conventional SST observations. The blended SST fields also clearly reveal the finescale spatial and temporal structures associated with coastal upwelling, demonstrating their utility in the analysis of finescale flows. With the high temporal resolution, the blended SST fie.
Quantifying single cell lipid signaling kinetics after photo-stimulation

Studying the role of molecularly distinct lipid species in cell signaling remains challenging due to a scarcity of methods for performing quantitative lipid biochemistry in living cells. We have recently used lipid uncaging to quantify lipid-protein affinities and rates of lipid transbilayer movement and turnover in the diacylglycerol signaling pathway using population average time series data. So far, this approach does not allow to account for the cell-to-cell variability of cellular signaling responses. We here report a framework that allows to uniquely identify model parameters such diacylglycerol-protein affinities and transbilayer movement rates at the single cell level for a broad variety of structurally different diacylglycerol species. We find that lipid unsaturation degree and longer side chains generally correlate with faster lipid transbilayer movement and turnover and higher lipid-protein affinities. In summary, our work demonstrates how rate parameters and lipid-protein affinities can be quantified from single cell signaling trajectories with sufficient sensitivity to resolve the subtle kinetic differences caused by the chemical diversity of cellular signaling lipid pools.