We present GLM-Dialog, a large-scale language model (LLM) with 10B parameters capable of knowledge-grounded conversation in Chinese using a search engine to access the Internet knowledge. GLM-Dialog offers a series of applicable techniques for exploiting various external knowledge including both helpful and noisy knowledge, enabling the creation of robust knowledge-grounded dialogue LLMs with limited proper datasets. To evaluate the GLM-Dialog more fairly, we also propose a novel evaluation method to allow humans to converse with multiple deployed bots simultaneously and compare their performance implicitly instead of explicitly rating using multidimensional metrics. Comprehensive evaluations from automatic to human perspective demonstrate the advantages of GLM-Dialog comparing with existing open source Chinese dialogue models. We release both the model checkpoint and source code, and also deploy it as a WeChat application to interact with users. We offer our evaluation platform online in an effort to prompt the development of open source models and reliable dialogue evaluation systems. All the source code is available on Github.

High-performance computing (HPC) systems are increasingly shared by a variety of data-and metadata-intensive parallel applications. However, existing parallel file systems employed for HPC storage management are unable to differentiate the I/O requests from concurrent applications and meet their different performance requirements. Previous work, vPFS, provided a solution to this problem by virtualizing a parallel file system and enabling proportional-share bandwidth allocation to the applications; but it cannot handle the increasingly diverse applications in today's HPC environments, including those that have different sizes of I/Os and those that are metadata-intensive. This paper presents vPFS+ which builds upon the virtualization framework provided by vPFS but addresses its limitations in supporting diverse HPC applications. First, a new proportional-share I/O scheduler, SFQ(D)+, is created to allow applications with various I/O sizes and issue rates to share the storage with good application-level fairness and system-level utilization. Second, vPFS+ extends the scheduling to also include metadata I/Os and provides performance isolation to metadata-intensive applications. vPFS+ is prototyped on PVFS2, a widely used open-source parallel file system, and evaluated using a comprehensive set of representative HPC benchmarks and applications (IOR, NPB BTIO, WRF, and multi-md-test). The results confirm that the new SFQ(D)+ scheduler can provide significantly better performance isolation to applications with small, bursty I/Os than the traditional SFQ(D) scheduler (3.35 times better) and the native PVFS2 (8.25 times better) while still making efficient use of the storage. The results also show that vPFS+ can deliver near-perfect proportional sharing (>95% of the target sharing ratio) to metadata-intensive applications.

Big-data systems are increasingly shared by diverse, data-intensive applications from different domains. However, existing systems lack the support for I/O management, and the performance of big-data applications degrades in unpredictable ways when they contend for I/Os. To address this challenge, this paper proposes IBIS, an Interposed Big-data I/O Scheduler, to provide I/O performance differentiation for competing applications in a shared big-data system. IBIS transparently intercepts, isolates, and schedules an application's different phases of I/Os via an I/O interposition layer on every datanode of the big-data system. It provides a new proportional-share I/O scheduler, SFQ(D2), to allow applications to share the I/O service of each datanode with good fairness and resource utilization. It enables the distributed I/O schedulers to coordinate with one another and to achieve proportional sharing of the big-data system's total I/O service in a scalable manner. Finally, it supports the shared use of big-data resources by diverse frameworks and manages the I/Os from different types of big-data workloads (e.g., batch jobs vs. queries) across these frameworks. The prototype of IBIS is implemented in Hadoop/YARN, a widely used big-data system. Experiments based on a variety of representative applications (WordCount, TeraSort, Facebook, TPC-H) show that IBIS achieves good total-service proportional sharing with low overhead in both application performance and resource usages. IBIS is also shown to support various performance policies: it can deliver stronger performance isolation than native Hadoop/YARN (99% better for WordCount and 15% better for TPC-H queries) with good resource utilization; and it can also achieve perfect proportional slowdown with better application performance (30% better than native Hadoop).

Big-data systems are increasingly important for solving the data-driven problems in many science domains including geosciences. However, existing big-data systems cannot support the self-describing data formats such as NetCDF which are commonly used by scientific communities for data distribution and sharing. This limitation presents a serious hurdle to the further adoption of big-data systems by science domains and prevents scientific users from leveraging these systems to improve their productivity. This paper presents a solution to this problem by enabling big-data systems to directly store and process scientific data. Specifically, it enables Hadoop to efficiently store NetCDF data on HDFS and process them in MapReduce using convenient APIs. It also enables Hive to support standard queries on NetCDF data, transparently to users. The paper also presents an evaluation of the proposed solution using several representative queries on a typical geoscientific dataset. The results show that the proposed approach achieves substantial speedup (up to 20 times) and space saving (83% reduction), compared to the traditional approach which has to convert NetCDF data to CSV format for Hadoop and Hive to use them.

Big-data systems are increasingly used in many disciplines for important tasks such as knowledge discovery and decision making by processing large volumes of data. Big-data systems rely on hard-disk drive (HDD) based storage to provide the necessary capacity. However, as big-data applications grow rapidly more diverse and demanding, HDD storage becomes insufficient to satisfy their performance requirements. Emerging solid-state drives (SSDs) promise great IO performance that can be exploited by big-data applications, but they still face serious limitations in capacity, cost, and endurance and therefore must be strategically incorporated into big-data systems. This paper presents BigCache, an SSD-based distributed caching layer for big-data systems. It is designed to be seamlessly integrated with existing big-data systems and transparently accelerate IOs for diverse big-data applications. The management of the distributed SSD caches in BigCache is coordinated with the job management of big-data systems in order to support cache-locality-driven job scheduling. BigCache is prototyped in Hadoop to provide caching upon HDFS for MapReduce applications. It is evaluated using typical MapReduce applications, and the results show that BigCache reduces the runtime of WordCount by 38% and the runtime of TeraSort by 52%. The results also show that BigCache is able to achieve significant speedup by caching only partial input for the benchmarks, owing to its ability to cache partial input and its replacement policy that recognizes application access patterns.

Due to the popularity and importance of Parallel File Systems (PFSs) in modern High Performance Computing (HPC) centers, PFS designs and I/O optimizations are active research topics. However, the research process is often time-consuming and faces cost and complexity challenges in deploying experiments in real HPC systems. This paper describes PFSsim, a trace-driven simulator of distributed storage systems that allows the evaluation of PFS designs, I/O schedulers, network structures, and workloads. PFSsim differentiates itself from related work in that it provides a powerful platform featuring a modular design with high flexibility in the modeling of subsystems including the network, clients, data servers and I/O schedulers. It does so by designing the simulator to capture abstractions found in common PFSs. PFSsim also exposes script-based interfaces for detailed configurations. Experiments and validation against real systems considering sub-modules and the entire simulator show that PFSsim is capable of simulating a representative PFS (PVFS2) and of modeling different I/O scheduler algorithms with good fidelity. In addition, the simulation speed is also shown to be acceptable.

Existing parallel file systems are unable to differentiate I/Os requests from concurrent applications and meet per-application bandwidth requirements. This limitation prevents applications from meeting their desired Quality of Service (QoS) as high-performance computing (HPC) systems continue to scale up. This paper presents vPFS, a new solution to address this challenge through a bandwidth virtualization layer for parallel file systems. vPFS employs user-level parallel file system proxies to interpose requests between native clients and servers and to schedule parallel I/Os from different applications based on configurable bandwidth management policies. vPFS is designed to be generic enough to support various scheduling algorithms and parallel file systems. Its utility and performance are studied with a prototype which virtualizes PVFS2, a widely used parallel file system. Enhanced proportional sharing schedulers are enabled based on the unique characteristics (parallel striped I/Os) and requirement (high throughput) of parallel storage systems. The enhancements include new threshold- and layout-driven scheduling synchronization schemes which reduce global communication overhead while delivering total-service fairness. An experimental evaluation using typical HPC benchmarks (IOR, NPB BTIO) shows that the throughput overhead of vPFS is small (;96% of target sharing ratio) for competing applications with diverse I/O patterns.