1,720,955 research outputs found
Optimized Forward-Backward Rematerialization for Memory-Efficient Pipeline Parallel Training
No full textPipeline parallelism is a key technique for scaling deep network training across multiple devices. Recent works have significantly reduced pipeline idle time by improving scheduling efficiency. Decoupling the computation of gradients with respect to weights and activations led to the development of schedules with almost no idle time. However, these methods still require substantial memory, limiting their applicability on resource-constrained hardware.Our first contribution is to introduce recomputation to the backward pass, extending rematerialization beyond the forward pass. This enables executing schedules with decoupled gradient computations under much tighter memory constraints. Our second contribution is a unified optimization approach that, given a model and hardware memory constraints, formulates and solves an Integer Linear Programming (ILP) problem to determine the optimal per-microbatch, per-GPU rematerialization strategy for a given schedule, applicable to both one-wave and multi-wave pipeline schedules. Our third contribution shows that, as device memory constraints vary, the relative advantages of different pipeline schedules also change in the presence of rematerialization. We provide corresponding insights and a PyTorch framework that enables finding and executing the optimal combination of pipeline scheduling and rematerialization strategies.Experiments demonstrate the effectiveness of all three contributions, showing that our approach enables efficient training of larger models under tight memory budgets, adapts optimally to varying memory capacities, and reduces recomputation overhead compared to existing recomputation solutions
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Optimized Forward-Backward Rematerialization for Memory-Efficient Pipeline Parallel Training
No full textPipeline parallelism is a key technique for scaling deep network training across multiple devices. Recent works have significantly reduced pipeline idle time by improving scheduling efficiency. Decoupling the computation of gradients with respect to weights and activations led to the development of schedules with almost no idle time. However, these methods still require substantial memory, limiting their applicability on resource-constrained hardware.Our first contribution is to introduce recomputation to the backward pass, extending rematerialization beyond the forward pass. This enables executing schedules with decoupled gradient computations under much tighter memory constraints. Our second contribution is a unified optimization approach that, given a model and hardware memory constraints, formulates and solves an Integer Linear Programming (ILP) problem to determine the optimal per-microbatch, per-GPU rematerialization strategy for a given schedule, applicable to both one-wave and multi-wave pipeline schedules. Our third contribution shows that, as device memory constraints vary, the relative advantages of different pipeline schedules also change in the presence of rematerialization. We provide corresponding insights and a PyTorch framework that enables finding and executing the optimal combination of pipeline scheduling and rematerialization strategies.Experiments demonstrate the effectiveness of all three contributions, showing that our approach enables efficient training of larger models under tight memory budgets, adapts optimally to varying memory capacities, and reduces recomputation overhead compared to existing recomputation solutions
Mind Bubbles and Memory: Bounds on Scheduling Pipeline Parallelism with Rematerialization
No full textTraining large neural networks, especially Transformer-based Large Language Models (LLMs), requires massive high-performance computing (HPC) resources. Within each microbatch, computations follow a strictly sequential flow through a stack of transformer blocks: a forward pass to compute the loss, and a backward pass to propagate gradients. This sequential structure limits intrinsic parallelism. To improve performance, several complementary strategies have been developed: data, tensor, sequence, and pipeline parallelism, typically combined to achieve scalability over tens of thousands of GPUs.This paper presents a formal analysis of pipeline parallelism (PP) for large-scale training. In PP, the model is partitioned into multiple stages, and microbatches are injected into the pipeline to overlap computation. The main challenge is to minimize idle periods (pipeline bubbles) while managing memory usage, since each GPU must store intermediate activations from multiple in-flight microbatches. Existing scheduling algorithms such as GPIPE, 1F1B, HANAYO, and MEGATRON reduce idle time but lack formal lower bounds or explicit modeling of memory constraints.We develop a unified analytical approach for PP scheduling, deriving lower bounds on completion time for both single-wave and multi-wave regimes. Our analysis explicitly incorporates a memory constraint K, denoting the number of activations that can be stored per GPU. Exact results are provided for two extreme cases (minimal memory (K = 1) and large memory (K ≥ m)), while general lower bounds are established for intermediate configurations. Our analysis highlights the intrinsic coupling between pipeline utilization and memory footprint, providing a foundation for evaluating and comparing pipeline scheduling algorithms under realistic memory constraints.</p
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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