Task Granularity Research Articles

A study on formalizing the knowledge of data curation activities across different fields.

In recent years, with the trend of open science, there have been many efforts to share research data on the internet. To promote research data sharing, data curation is essential to make the data interpretable and reusable. In research fields such as life sciences, earth sciences, and social sciences, tasks and procedures have been already developed to implement efficient data curation to meet the needs and customs of individual research fields. However, not only data sharing within research fields but also interdisciplinary data sharing is required to promote open science. For this purpose, knowledge of data curation across the research fields is surveyed, analyzed, and organized as an ontology in this paper. As the survey, existing vocabularies and procedures are collected and compared as well as interviews with the data curators in research institutes in different fields are conducted to clarify commonalities and differences in data curation across the research fields. It turned out that the granularity of tasks and procedures that constitute the building blocks of data curation is not formalized. Without a method to overcome this gap, it will be challenging to promote interdisciplinary reuse of research data. Based on the analysis above, the ontology for the data curation process is proposed to describe data curation processes in different fields universally. It is described by OWL and shown as valid and consistent from the logical viewpoint. The ontology successfully represents data curation activities as the processes in the different fields acquired by the interviews. It is also helpful to identify the functions of the systems to support the data curation process. This study contributes to building a knowledge framework for an interdisciplinary understanding of data curation activities in different fields.

Future applications of generative large language models: A data-driven case study on ChatGPT

This study delves into the evolving role of generative Large Language Models (LLMs). We develop a data-driven approach to collect and analyse tasks that users are asking to generative LLMs. Thanks to the focus on tasks this paper contributes to give a quantitative and granular understanding of the potential influence of LLMs in different business areas. Utilizing a dataset comprising over 3.8 million tweets, we identify and cluster 31,747 unique tasks, with a specific case study on ChatGPT. To reach this goal, the proposed method combines two Natural Language Processing (NLP) Techniques, Named Entity Recognition (NER) and BERTopic. The combination makes it possible to collect granular tasks of LLMs (NER) and clusters them in business areas (BERTopic). Our findings reveal a wide spectrum of applications, from programming assistance to creative content generation, highlighting LLM's versatility. The analysis highlighted six emerging areas of application for ChatGPT: human resources, programming, social media, office automation, search engines, education. The study also examines the implications of these findings for innovation management, proposing a research agenda to explore the intersection of the identified areas, with four stages of the innovation process: idea generation, screening/idea selection, development, and diffusion/sales/marketing.

A Task-Interdependency Model of Complex Collaboration Towards Human-Centered Crowd Work (Extended Abstract)

Mathematical models of crowdsourcing and human computation today largely assume small modular tasks, "computational primitives" such as labels, comparisons, or votes requiring little coordination. However, while these models have successfully shown how crowds can accomplish significant objectives, they can inadvertently advance a less than human view of crowd workers where workers are treated as low skilled, replaceable, and untrustworthy, carrying out simple tasks in online labor markets for low pay under algorithmic management. They also fail to capture the unique human capacity for complex collaborative work where the main concerns are how to effectively structure, delegate, and collaborate on work that may be large in scope, underdefined, and highly interdependent. We present a model centered on interdependencies—a phenomenon well understood to be at the core of collaboration—that allows one to formally reason about diverse challenges to complex collaboration. Our model represents tasks as an interdependent collection of subtasks, formalized as a task graph. Each node is a subtask with an arbitrary size parameter. Interdependencies, represented as node and edge weights, impose costs on workers who need to spend time absorbing context of relevant work. Importantly, workers do not have to pay this context cost for work they did themselves. To illustrate how this simple model can be used to reason about diverse aspects of complex collaboration, we apply the model to diverse aspects of complex collaboration. We examine the limits of scaling complex crowd work, showing how high interdependencies and low task granularity bound work capacity to a constant factor of the contributions of top workers, which is in turn limited when workers are short-term novices. We examine recruitment and upskilling, showing the outsized role top workers play in determining work capacity, and surfacing insights on situated learning through a stylized model of legimitate peripheral participation (LPP). Finally, we turn to the economy as a setting where complex collaborative work already exists, using our model to explore the relationship between coordination intensity and occupational wages. Using occupational data from O*NET and the Bureau of Labor Statistics, we introduce a new index of occupational coordination intensity and validate the predicted positive correlation. We find preliminary evidence that higher coordination intensity occupations are more resistant to displacement by AI based on historical growth in automation and OpenAI data on LLM exposure. Our hope is to spur further development of models that emphasize the collaborative capacities of human workers, bridge models of crowd work and traditional work, and promote AI in roles augmenting human collaboration. The full paper can be found at: https://doi.org/10.48550/arXiv.2309.00160.

Enhancing Machine Translation Quality Estimation via Fine-Grained Error Analysis and Large Language Model

Fine-grained error span detection is a sub-task within quality estimation that aims to identify and assess the spans and severity of errors present in translated sentences. In prior quality estimation, the focus has predominantly been on evaluating translations at the sentence and word levels. However, such an approach fails to recognize the severity of specific segments within translated sentences. To the best of our knowledge, this is the first study that concentrates on enhancing models for this fine-grained error span detection task in machine translation. This study introduces a framework that sequentially performs sentence-level error detection, word-level error span extraction, and severity assessment. We present a detailed analysis for each of the methodologies we propose, substantiating the effectiveness of our system, focusing on two language pairs: English-to-German and Chinese-to-English. Our results suggest that task granularity enhances performance and that a prompt-based fine-tuning approach can offer optimal performance in the classification tasks. Furthermore, we demonstrate that employing a large language model to edit the fine-tuned model’s output constitutes a top strategy for achieving robust quality estimation performance.

Fast Progressive Differentiable Architecture Search based on adaptive task granularity reorganization

Shrinkage methods reduce the search space of a Differentiable Architecture Search (DARTS) by progressively discarding candidates, which accelerates the search speed. However, their shrinkage strategy suffers from the vulnerability of too fine task granularity. In other words, they drop only one of the least promising candidates per round of shrinkage, which is suboptimal in terms of performance and efficiency. In this study, we introduce the concept of Granular Computing (GrC) into the shrinkage method and present a Fast Progressive Differentiable Architecture Search (FP-DARTS) method. This method effectively reduces the computational complexity of each round of shrinkage, thereby improving the efficiency and performance of the algorithm. FP-DARTS can be divided into three stages: adaptive granularity division and selection, granular-channel performance evaluation, and progressive shrinkage. In the first stage, to reorganize the task granularity, we cluster the candidate operations into granular-channels and adaptively select the appropriate task granularity. We also propose a dynamic clustering strategy to avoid introducing additional computation. In the second stage, we train the architecture parameters to measure the potential of the granular-channels. In the third stage, to improve the stability of the shrinkage results, we introduce a channel annealing mechanism to smoothly discard unpromising granular-channels. We conducted systematic experiments on CIFAR-10 and ImageNet and achieved a test accuracy of 97.56% on CIFAR-10 with 0.04 GPU-days, and a test accuracy of 75.5% on ImageNet with 1.2 GPU-days. We also conducted experiments on the search space of NAS-Bench-201, and obtained test accuracies of 94.22, 73.07, and 46.23% for CIFAR-10, CIFAR-100 and ImageNet16-120, respectively. The above experimental results demonstrate that FP-DARTS achieves higher search speed and competitive performance compared to other state-of-the-art shrinkage methods and non-shrinkage methods.

Code modernization strategies for short-range non-bonded molecular dynamics simulations

Modern HPC systems are increasingly relying on greater core counts and wider vector registers. Thus, applications need to be adapted to fully utilize these hardware capabilities. One class of applications that can benefit from this increase in parallelism are molecular dynamics simulations. In this paper, we describe our efforts at modernizing the ESPResSo++ simulation package for molecular dynamics by restructuring its particle data layout for efficient memory accesses and applying vectorization techniques to benefit the calculation of short-range non-bonded forces, which results in an overall three times speedup and serves as a baseline for further optimizations. We also implement fine-grained parallelism for multi-core CPUs through HPX, a C++ runtime system which uses lightweight threads and an asynchronous many-task approach to maximize concurrency. Our goal is to evaluate the performance of an HPX-based approach compared to the bulk-synchronous MPI-based implementation. This requires the introduction of an additional layer to the domain decomposition scheme that defines the task granularity. On spatially inhomogeneous systems, which impose a corresponding load-imbalance in traditional MPI-based approaches, we demonstrate that by choosing an optimal task size, the efficient work-stealing mechanisms of HPX can overcome the overhead of communication resulting in an overall 1.4 times speedup compared to the baseline MPI version.

Immediate Learning Benefits of Retrieval Tasks

Abstract: We investigated immediate learning benefits of retrieval tasks when followed by a self-regulated relearning opportunity, compared to a restudy task. We aimed to unravel the underlying metacognitive and motivational mechanisms. In a mixed-factorial design ( N = 104), we manipulated review task (retrieval vs. restudy) as a within-subjects factor in two experimental sessions, and task granularity (coarse- vs. fine-grained) as a between-subjects factor. The retrieval task led to an immediate learning benefit compared to a restudy task in Session 1, but not across sessions. The review-task order influenced the emergence of an immediate retrieval-practice effect. Mediation analyses for Session 1 revealed that decreased levels of both judgments of learning (JOL) and self-efficacy partially mediated the retrieval-task effect on the learning outcome. Fine-grained tasks increased JOL regulation accuracy; however, this increase did not translate into better relearning. Retrieval tasks can improve learning outcomes, also after short delays, specifically when relearning opportunities are provided.

The Tiny-Tasks Granularity Trade-Off: Balancing Overhead Versus Performance in Parallel Systems

Models of parallel processing systems typically assume that one has <inline-formula><tex-math notation="LaTeX">$l$</tex-math></inline-formula> workers and jobs are split into an equal number of <inline-formula><tex-math notation="LaTeX">$k=l$</tex-math></inline-formula> tasks. Splitting jobs into <inline-formula><tex-math notation="LaTeX">$k &gt; l$</tex-math></inline-formula> smaller tasks, i.e. using “tiny tasks”, can yield performance and stability improvements because it reduces the variance in the amount of work assigned to each worker, but as <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> increases, the overhead involved in scheduling and managing the tasks begins to overtake the performance benefit. We perform extensive experiments on the effects of task granularity on an Apache Spark cluster, and based on these, develop a four-parameter model for task and job overhead that, in simulation, produces sojourn time distributions that match those of the real system. We also present analytical results which illustrate how using tiny tasks improves the stability region of split-merge systems, and analytical bounds on the sojourn and waiting time distributions of both split-merge and single-queue fork-join systems with tiny tasks. Finally we combine the overhead model with the analytical models to produce an analytical approximation to the sojourn and waiting time distributions of systems with tiny tasks which include overhead. We also perform analogous tiny-tasks experiments on a hybrid multi-processor shared memory system based on MPI and OpenMP which has no load-balancing between nodes. Though no longer strict analytical bounds, our analytical approximations with overhead match both the Spark and MPI/OpenMP experimental results very well.

Exploring granular test coverage and its evolution with matrix visualizations

Current software-development tools that are used in practice make understanding the test execution of software difficult, for both granular tasks ( e.g., answering questions such as, “which test cases execute this method?”) and global tasks ( e.g., answering questions such as, “what is the proportion of unit tests to system tests?”). Current tools typically support local, file-based views of a project’s test suite and its execution data, and rarely offer a global overview. Even more rarely, do they provide access to historical information of this nature. Such global overviews can provide a larger context for a method’s execution by test cases; help identify other similar, or related methods; and even reveal similarity between individual tests. This work approaches such challenges with a novel, interactive, matrix-based visual interface that provides a global overview of a software project’s test suite, specifically in the context of the methods available in the project’s codebase. Through a series of interactive functions to sort, filter, query, and explore a test-matrix visualization, we evaluate how developers can effectively answer questions about their project’s test suite, and the code executed by such tests. We built a dynamic test-suite analysis and software-visualization tool that implements our designed interface to address the challenges of understanding the testing of software systems. With this implementation, we conducted a user study of 20 software developers to assess their ability to understand and report test execution information and measured accuracy and time. Additionally, we present a series of case studies to demonstrate a number of insights that our tool reveals. Our evaluations, performed on 26 real-world software systems, show that the interactive visualization assisted developers to answer questions about software tests and the code they execute. Further, the visualization consistently outperforms traditional development tools, both in accuracy and time taken to complete software-engineering tasks. Global-overview test matrices offer novel perspectives on test-suite composition, which can guide software development and testing practices.

DTBVis: An interactive visual comparison system for digital twin brain and human brain

The digital twin brain (DTB) computing model from brain-inspired computing research is an emerging artificial intelligence technique, which is realized by a computational modeling approach of hardware and software. It can achieve various cognitive abilities and their synergistic mechanisms in a manner similar to the human brain. Given that the task of the DTB is to simulate the functions of the human brain, comparing the similarities and differences between the two is crucial. However, the visualization study of the DTB is still under-researched. Moreover, the complexity of the datasets (multilevel spatiotemporal granularity and different types of comparison tasks) presents new challenges to the analysis and exploration of visualization. Therefore, in this study, we proposed DTBVis, a visual analytics system that supports comparison tasks for the DTB. DTBVis supports iterative explorations from different levels and at different granularities. Combined with automatic similarity recommendation, and high-dimensional exploration, DTBVis can assist experts in understanding the similarities and differences between the DTB and the human brain, thus helping them adjust their model and enhance its functionality. The highest level of DTBVis shows an overview of the datasets from the brain, which is used for comparison and exploration of the function and structure of the DTB and the human brain. The medium level is used for the comparison and exploration of a designated brain region. The low level can analyze a designated brain voxel. We worked closely with experts of brain science and held regular seminars with them. Feedback from the experts indicates that our approach helps them conduct comparative studies of the DTB and human brain and make modeling adjustments of the DTB through intuitive visual comparisons and interactive explorations.

Startup-aware Dependent Task Scheduling with Bandwidth Constraints in Edge Computing

In edge computing, applications can be scheduled in the granularity of inter-dependent tasks to proximate edge servers to achieve high performance. Before execution, the edge server must initialize the corresponding runtime environment, named task startup. However, existing studies on dependent task scheduling severely ignore bandwidth constraints during task startups, which is impractical and incurs a long startup latency. To fill in this gap, we first model the task startup process with bandwidth constraints on edge servers. Then, we formulate the dependent task scheduling problem with startup latency in heterogeneous edge computing. To efficiently generate schedules and satisfy the real-time requirements in edge computing, a novel low-complexity list scheduling algorithm integrated with cloud clone, Startup-aware Dependent Task Scheduling (SDTS), is proposed. Constrained by bandwidth and computation resources, SDTS first coordinates task startup, dependent data transmission, and task execution to optimize each task’s finish time. Then, a cloud clone for each task is deployed to utilize scalable resources and initialized runtime environments. Furthermore, task scheduling refinement is designed to release the bandwidth and computation resources consumed by redundant tasks and improve the schedule. Extensive simulations based on real-world datasets show that SDTS substantially reduces 30%-60% makespan compared with existing baselines.

Optimizing Iterative Data-flow Scientific Applications using Directed Cyclic Graphs

Data-flow programming models have become a popular choice for writing parallel applications as an alternative to traditional work-sharing parallelism. They are better suited to write applications with irregular parallelism that can present load imbalance. However, these programming models suffer from overheads related to task creation, scheduling and dependency management, limiting performance and scalability when tasks become too small. At the same time, many HPC applications implement iterative methods or multi-step simulations that create the same directed acyclic graphs of tasks on each iteration. By giving application programmers a way to express that a specific loop is creating the same task pattern on each iteration, we can create a single task DAG once and transform it into a cyclic graph. This cyclic graph is then reused for successive iterations, minimizing task creation and dependency management overhead. This paper presents the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">taskiter</i> , a new construct we propose for the OmpSs-2 and OpenMP programming models, allowing the use of directed cyclic task graphs (DCTG) to minimize runtime overheads. Moreover, we present a simple <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">immediate successor</i> locality-aware heuristic that minimizes task scheduling overhead by bypassing the runtime task scheduler. We evaluate the implementation of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">taskiter</i> and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">immediate successor</i> heuristic in 8 iterative benchmarks. Using small task granularities, we obtain a geometric mean speedup of 2.56x over the reference OmpSs-2 implementation, and a 3.77x and 5.2x speedup over the LLVM and GCC OpenMP runtimes, respectively.

Dynamic Resource Allocation Using an Adaptive Multi-Objective Teaching-Learning Based Optimization Algorithm in Cloud

Resource allocation is a non-polynomial complete problem in the cloud data center that selects the proper resources to execute many fine computational granularity tasks. Customer requirements and capacity of applications change frequently. To bridge the gap between frequently changing customer requirement and available infrastructure for the services, we propose a dynamic resource allocation strategy using an adaptive multi-objective teaching-learning based optimization (AMO-TLBO) algorithm in Cloud computing. To improve the exploration and exploitation capacities, AMO-TLBO introduces the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. Moreover, a grid-based approach to adaptively assess the non-dominated solutions maintained in an external archive is used. The objectives of AMO-TLBO include minimizing makespan, cost and maximizing utilization using well-balanced load across virtual machines. The evaluation results show that the proposed algorithm outperforms TLBO, MOPSO and NSGA-II algorithms in terms of different performance metrics.

Parallel QR Factorization of Block Low-rank Matrices

We present two new algorithms for Householder QR factorization of Block Low-Rank (BLR) matrices: one that performs block-column-wise QR and another that is based on tiled QR. We show how the block-column-wise algorithm exploits BLR structure to achieve arithmetic complexity of 𝒪( mn ), while the tiled BLR-QR exhibits 𝒪( mn 1.5 complexity. However, the tiled BLR-QR has finer task granularity that allows parallel task-based execution on shared memory systems. We compare the block-column-wise BLR-QR using fork-join parallelism with tiled BLR-QR using task-based parallelism. We also compare these two implementations of Householder BLR-QR with a block-column-wise Modified Gram–Schmidt (MGS) BLR-QR using fork-join parallelism and a state-of-the-art vendor-optimized dense Householder QR in Intel MKL. For a matrix of size 131k × 65k, all BLR methods are more than an order of magnitude faster than the dense QR in MKL. Our methods are also robust to ill conditioning and produce better orthogonal factors than the existing MGS-based method. On a CPU with 64 cores, our parallel tiled Householder and block-column-wise Householder algorithms show a speedup of 50 and 37 times, respectively.

Multistage Cloud-Service Matching and Optimization Based on Hierarchical Decomposition of Design Tasks

In cloud manufacturing systems, the multi-granularity of service resource and design task models leads to the complexity of cloud service matching. In order to satisfy the preference of resource requesters for large-granularity service resources, we propose a multistage cloud-service matching strategy to solve the problem of matching tasks and resources with different granularity sizes. First, a multistage cloud-service matching framework is proposed, and the basic strategy of matching tasks with cloud services is planned. Then, the context-aware task-ontology modeling method is studied, and a context-related task-ontology model is established. Thirdly, a process-decomposition method of design tasks is studied, and the product development process with small granularity tasks is established. Fourthly, a matching strategy of ontology tasks and cloud services is studied, and the preliminary matching is accomplished. Finally, intelligent optimization is carried out, and the optimal cloud service composition is found with the optimal design period as the objective function. With the help of the preceding method, the service matching of maximizing the task granularity is realized on the premise of ensuring the matching success rate, which meets the preference of resource requesters for large-granularity service resources.

Quantifying effects of tasks on group performance in social learning

Social learning is a learning process in which new behaviors can be acquired by observing and imitating others. It is the key to cultural evolution because individuals can exchange profitable information culturally within the group. Recent studies have over-focused on social learning strategies but paid rare attention to the learning tasks. In particular, in these studies, individuals rely on perfect imitation, directly copying the solutions of others, to improve their performance. However, imperfect imitation, a prevalent form of social learning in cultural evolution, has received little discussion. In this paper, the effects of three task features (task types, task complexity, and task granularity) on group performance are simulated with an agent-based model and quantified with decision trees. In the proposed model, individuals in a network learn from others via imperfect imitation, which means individuals make a trade-off between their solutions and socially acquired solutions. Here, status quo bias is introduced to represent the degree to which individuals adhere to their solutions. Results show that the performance of a group is not affected by task complexity in hard-to-easy tasks but declines with the task complexity rising in easy-to-hard tasks. Besides, groups usually perform better in fine-grained tasks than in coarse-grained ones. The main reason is that in coarse-grained tasks, conservative individuals encounter learning bottlenecks that prevent them from exploring superior solutions further. Interestingly, increasing task granularity can mitigate this disadvantage for conservative individuals. Most strikingly, the importance scores given by decision trees suggest that tasks play a decisive role in social learning. These findings provide new insights into social learning and have broad implications for cultural evolution.

Critical Section Overhead Reduction for OpenMP Program by Nesting a Serial Loop to Increase Task Granularity of Parallel Loop

This paper presents a simple method to reduce performance loss due to a parallel program's massive critical sections of parallel numerical integration. The method is to transform a fine grain parallel loop into a coarse grain parallel loop which is nesting a sequential loop. The coarse grain parallel loop is by nesting a loop block to make task granularities coarser than that naive one. In addition to the overhead reduction, the method makes the parallel work fraction significantly larger than the serial fraction. As a result, nesting a serial loop within a parallel loop improves the parallel program’s performance. Compared to the naïve method, which does not scale performance of parallel program of numerical integration, the nesting serial loop method scales a parallel program up to 3.26 times fold relative to its sequential program on quad-core processor. This result shows that the proposed method makes parallel program much faster compared to the naïve method.&#x0D;

From Hand to Eye: a Meta-Analysis of the Benefit from Handwriting Training in Visual Graph Recognition

Handwriting (HW) training seems to boost recognition of visual graphs and learning to read more than other learning experiences. However, effects across studies appear to be variable and the underlying cognitive mechanism has been elusive. We thus conducted a meta-analysis on 50 independent experiments (with 1525 participants) to determine the magnitude of this HW benefit in visual graph recognition, while enlightening the underlying cognitive mechanism, by investigating four types of moderators: training program (type of control training, presence/absence of phonological training, and HW tasks adopted); set size and training regime (duration and frequency of session and total amount of training); granularity of visual discrimination and perceptual learning tasks; and age of participants. The benefit from HW training was moderate-to-large and significant (Hedge’s g = 0.58, SE = .09) and was also modulated by the type of control training (larger relative to motor, g = 0.78, than to visual control, g = 0.37), phonological training (larger when it was absent, g = 0.79, than present, g = 0.47), and granularity of visual discrimination (larger for fine-grained, g = 0.93, than coarse-grained, g = 0.19). These results seem consistent with symbolic accounts that hold that the advantage from HW training in visual graph recognition is about perceptual learning rather than the motor act. Multiple meta-regressions also revealed that training regime moderated the HW benefit. We conclude that HW training is effective to improve visual graph recognition, and hence is still relevant for literacy instruction in the present digital era.

Three practical workflow schedulers for easy maximum parallelism

AbstractRuntime scheduling and workflow systems are an increasingly popular algorithmic component in HPC because they allow full system utilization with relaxed synchronization requirements. There are so many special‐purpose tools for task scheduling, one might wonder why more are needed. Use cases seen on the Summit supercomputer needed better integration with MPI and greater flexibility in job launch configurations. Preparation, execution, and analysis of computational chemistry simulations at the scale of tens of thousands of processors revealed three distinct workflow patterns. A separate job scheduler was implemented for each one using extremely simple and robust designs: file‐based, task‐list based, and bulk‐synchronous. Comparing to existing methods shows unique benefits of this work, including simplicity of design, suitability for HPC centers, short startup time, and well‐understood per‐task overhead. All three new tools have been shown to scale to full utilization of Summit, and have been made publicly available with tests and documentation. This work presents a complete characterization of the minimum effective task granularity for efficient scheduler usage scenarios. These schedulers have the same bottlenecks, and hence similar task granularities as those reported for existing tools following comparable paradigms.

Technology-Mediated Citizen Science Participation: A Motivational Model

We propose and test a framework of the antecedents of contribution in two technology-mediated citizen science projects, with different degrees of task granularity. Comparing earlier findings on the motivations of volunteers in a web-based image analysis project (high granularity), with new findings on the motivations of volunteers in a volunteer computing project (low granularity), we found that participation task granularity is correlated with motivation levels. Further, we found that collective and intrinsic motives are the most salient motivational factors, whereas reward motives are less important for volunteers. Intrinsic, norm-oriented and reputation-seeking motives were most strongly associated with participation intentions, which were, in turn, associated with participation. Finally, comparing the relationship between motives and participation among the two volunteer populations, we found that active-participation volunteers are characterized by significantly stronger association between collective motives and contribution intention, whereas passive-participation volunteers are characterized by significantly stronger association between identification with the community and contribution intention. Implications for research and practice are discussed.