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Miguel Castro

Publications and source records attributed to Miguel Castro.

10 recordsLinked to original sources

A global evidence map of human well-being and biodiversity co-benefits and trade-offs of natural climate solutions

Natural climate solutions (NCS) are critical for mitigating climate change through ecosystem-based carbon removal and emissions reductions. NCS implementation can also generate biodiversity and human well-being co-benefits and trade-offs ("NCS co-impacts"), but the volume of evidence on NCS co-impacts has grown rapidly across disciplines, is poorly understood, and remains to be systematically collated and synthesized. A global evidence map of NCS co-impacts would overcome key barriers to NCS implementation by providing relevant information on co-benefits and trade-offs where carbon mitigation potential alone does not justify NCS projects. We employ large language models to assess over two million articles, finding 257,266 relevant articles on NCS co-impacts. We analyze this large and dispersed body of literature using innovative machine learning methods to extract relevant data (e.g., study location, species, and other key variables), and create a global evidence map on NCS co-impacts. Evidence on NCS co-impacts has grown approximately ten-fold in three decades, although some of the most abundant evidence is associated with pathways that have less mitigation potential. We find that studies often examine multiple NCS pathways, indicating natural NCS pathway complements, and each NCS is often associated with two or more coimpacts. Finally, NCS co-impacts evidence and priority areas for NCS are often mismatched--some countries with high mitigation potential from NCS have few published studies on the broader co-impacts of NCS implementation. Our work advances and makes available novel methods and systematic and representative data of NCS co-impacts studies, thus providing timely insights to inform NCS research and action globally.

physics.soc-ph

Rethinking Machine Learning Collective Communication as a Multi-Commodity Flow Problem

We show communication schedulers' recent work proposed for ML collectives does not scale to the increasing problem sizes that arise from training larger models. These works also often produce suboptimal schedules. We make a connection with similar problems in traffic engineering and propose a new method, TECCL, that finds better quality schedules (e.g., finishes collectives faster and/or while sending fewer bytes) and does so more quickly on larger topologies. We present results on many different GPU topologies that show substantial improvement over the state-of-the-art.

cs.NI

Honeycomb: ordered key-value store acceleration on an FPGA-based SmartNIC

In-memory ordered key-value stores are an important building block in modern distributed applications. We present Honeycomb, a hybrid software-hardware system for accelerating read-dominated workloads on ordered key-value stores that provides linearizability for all operations including scans. Honeycomb stores a B-Tree in host memory, and executes SCAN and GET on an FPGA-based SmartNIC, and PUT, UPDATE and DELETE on the CPU. This approach enables large stores and simplifies the FPGA implementation but raises the challenge of data access and synchronization across the slow PCIe bus. We describe how Honeycomb overcomes this challenge with careful data structure design, caching, request parallelism with out-of-order request execution, wait-free read operations, and batching synchronization between the CPU and the FPGA. For read-heavy YCSB workloads, Honeycomb improves the throughput of a state-of-the-art ordered key-value store by at least 1.8x. For scan-heavy workloads inspired by cloud storage, Honeycomb improves throughput by more than 2x. The cost-performance, which is more important for large-scale deployments, is improved by at least 1.5x on these workloads.

cs.DC

HammingMesh: A Network Topology for Large-Scale Deep Learning

Numerous microarchitectural optimizations unlocked tremendous processing power for deep neural networks that in turn fueled the AI revolution. With the exhaustion of such optimizations, the growth of modern AI is now gated by the performance of training systems, especially their data movement. Instead of focusing on single accelerators, we investigate data-movement characteristics of large-scale training at full system scale. Based on our workload analysis, we design HammingMesh, a novel network topology that provides high bandwidth at low cost with high job scheduling flexibility. Specifically, HammingMesh can support full bandwidth and isolation to deep learning training jobs with two dimensions of parallelism. Furthermore, it also supports high global bandwidth for generic traffic. Thus, HammingMesh will power future large-scale deep learning systems with extreme bandwidth requirements.

cs.DC

IA-CCF: Individual Accountability for Permissioned Ledgers

Permissioned ledger systems allow a consortium of members that do not trust one another to execute transactions safely on a set of replicas. Such systems typically use Byzantine fault tolerance (BFT) protocols to distribute trust, which only ensures safety when fewer than 1/3 of the replicas misbehave. Providing guarantees beyond this threshold is a challenge: current systems assume that the ledger is corrupt and fail to identify misbehaving replicas or hold the members that operate them accountable -- instead all members share the blame. We describe IA-CCF, a new permissioned ledger system that provides individual accountability. It can assign blame to the individual members that operate misbehaving replicas regardless of the number of misbehaving replicas or members. IA-CCF achieves this by signing and logging BFT protocol messages in the ledger, and by using Merkle trees to provide clients with succinct, universally-verifiable receipts as evidence of successful transaction execution. Anyone can audit the ledger against a set of receipts to discover inconsistencies and identify replicas that signed contradictory statements. IA-CCF also supports changes to consortium membership and replicas by tracking signing keys using a sub-ledger of governance transactions. IA-CCF provides strong disincentives to misbehavior with low overhead: it executes 47,000 tx/s while providing clients with receipts in two network round trips.

cs.DC

Predicting post-operative right ventricular failure using video-based deep learning

Non-invasive and cost effective in nature, the echocardiogram allows for a comprehensive assessment of the cardiac musculature and valves. Despite progressive improvements over the decades, the rich temporally resolved data in echocardiography videos remain underutilized. Human reads of echocardiograms reduce the complex patterns of cardiac wall motion, to a small list of measurements of heart function. Furthermore, all modern echocardiography artificial intelligence (AI) systems are similarly limited by design - automating measurements of the same reductionist metrics rather than utilizing the wealth of data embedded within each echo study. This underutilization is most evident in situations where clinical decision making is guided by subjective assessments of disease acuity, and tools that predict disease onset within clinically actionable timeframes are unavailable. Predicting the likelihood of developing post-operative right ventricular failure (RV failure) in the setting of mechanical circulatory support is one such clinical example. To address this, we developed a novel video AI system trained to predict post-operative right ventricular failure (RV failure), using the full spatiotemporal density of information from pre-operative echocardiography scans. We achieve an AUC of 0.729, specificity of 52% at 80% sensitivity and 46% sensitivity at 80% specificity. Furthermore, we show that our ML system significantly outperforms a team of human experts tasked with predicting RV failure on independent clinical evaluation. Finally, the methods we describe are generalizable to any cardiac clinical decision support application where treatment or patient selection is guided by qualitative echocardiography assessments.

cs.CV

Fast General Distributed Transactions with Opacity using Global Time

Transactions can simplify distributed applications by hiding data distribution, concurrency, and failures from the application developer. Ideally the developer would see the abstraction of a single large machine that runs transactions sequentially and never fails. This requires the transactional subsystem to provide opacity (strict serializability for both committed and aborted transactions), as well as transparent fault tolerance with high availability. As even the best abstractions are unlikely to be used if they perform poorly, the system must also provide high performance. Existing distributed transactional designs either weaken this abstraction or are not designed for the best performance within a data center. This paper extends the design of FaRM - which provides strict serializability only for committed transactions - to provide opacity while maintaining FaRM's high throughput, low latency, and high availability within a modern data center. It uses timestamp ordering based on real time with clocks synchronized to within tens of microseconds across a cluster, and a failover protocol to ensure correctness across clock master failures. FaRM with opacity can commit 5.4 million neworder transactions per second when running the TPC-C transaction mix on 90 machines with 3-way replication.

cs.DC

A1: A Distributed In-Memory Graph Database

A1 is an in-memory distributed database used by the Bing search engine to support complex queries over structured data. The key enablers for A1 are availability of cheap DRAM and high speed RDMA (Remote Direct Memory Access) networking in commodity hardware. A1 uses FaRM as its underlying storage layer and builds the graph abstraction and query engine on top. The combination of in-memory storage and RDMA access requires rethinking how data is allocated, organized and queried in a large distributed system. A single A1 cluster can store tens of billions of vertices and edges and support a throughput of 350+ million of vertex reads per second with end to end query latency in single digit milliseconds. In this paper we describe the A1 data model, RDMA optimized data structures and query execution.

cs.DB

Anisotropic normal-state properties of the MgB2 superconductor

Based on the experimentally found existence of two gaps in MgB2 (one gap associated to the boron sigma-states and the other to the boron pi-states), the different contributions to the transport properties, electrical conductivity and Hall coefficient, were studied using the full potential-linearized augmented plane wave method and the generalized gradient approximation. MgB2 doping was analyzed in the rigid band approximation. This permitted the study of the partial substitution of magnesium for aluminium (Mg1-xAlxB2) as well as other substitutions such as AB2 (A=Be, Zr, Nb and Ta). The sigma-bands (boron sigma-states), which are associated to the large superconducting gap, are very anisotropic at EF, while the pi-bands have very little anisotropic character. In (Mg1-xAlxB2) Tc diminishes with Al content, the other compounds are not superconductors. In this work it was found that with electron doping, such as Al substitution, the sigma-band conductivity decreases and the corresponding bands become less anisotropic. sigma-band contribution for BeB2 and ScB2 at EF is very small and the anisotropy is much lower. For Zr, Nb and Ta there are no sigma-bands at EF. These results give a connection between superconductivity and the character of the sigma-band; band conductivity and band anisotropy. This gives a plausible explanation for the diminution of Tc with different doping of MgB2.

cond-mat.supr-con

Comparative Study of the Electronic Structure of Alkaline-earth Borides (MeB2; Me=Mg, Al, Zr, Nb, and Ta) and their Normal-State Conductivity

By means of density functional theory the electronic structure of the MgB2 superconductor was characterised and compared with that of the related iso-structural systems: AlB2, ZrB2, NbB2, and TaB2. Using the full-potential linearized augmented plane waves (FP-LAPW) method and the generalised gradient approximation, the electronic density distribution, density of states, and band structures were obtained for these compounds. The electrical conductivity, which cannot be easily measured in the c-direction, was calculated, in the relaxation time approximation using band structure results. It was found that the two-dimensional (2D) crystal structure character of these metallic diborides is also reflected in the electronic charge distribution. This 2D pattern is not completely seen in the electrical conductivity as it is, for instance, in the superconductor high Tc cuprates. Indeed, it was found that, by the electrical conductivity calculations, all these compounds have a bulk, yet anisotropic, conductivity.

cond-mat.supr-con