ラズパイでLustre、分散ファイルシステムを構築して、I/Oのバンド幅を爆上げしたいって話

まずはいきなり参考文献ですね。 GlusterFS

んで、早速壁にぶち当たるわけですね。Lustreはbinaryが配布されているらしいんですが、x86用らしいんですよね。ということで、raspiで動かすためにはソースコードをビルドしないといけないんですね。

まあ、これだけ書いて終わりっていうの残念なので、分散ファイルシステムについて知っていることを書いていきたいと思います。

まず、ファイルシステムっていうのは、OSの機能で、ブロックデバイスの操作をするためのデバイスドライバをラッピングしてくれているOSの機能。デバイスドライバへのAPIを提供してくれているOSの機能。で、ファイルシステムがあるから、我々はOSにお願いするだけでいい感じにファイルを作れるし、どこにどのファイルがあるか、とかそういう管理もしなくていいのよね。ただね、ファイルシステムもただのプログラムに過ぎないんですわ。何が言いたいかというと、内部ではカチカチとデバイスドライバーを動かしているわけで。いや、何が言いたいかっていうと、1ファイルにつき1プロセスしか操作を許されないってことね。つまり、複数のプロセスが一気に一つのファイルに書き込むとか、そういうのは無理なわけよ。これまず大事なポイントね。だからね、書き込みたいデータがたくさんあってもsingule writingをするしかないんだよね。 はい、GPT先生に聞いてみた。

Is it impossible for multiple processes to write data to a same file simultaniously?

In traditional file systems, it is generally not possible for multiple processes to write data simultaneously to the same file without coordination or synchronization mechanisms. Concurrent writes to the same file can lead to data corruption, inconsistencies, or conflicts.

When multiple processes attempt to write to the same file simultaneously, several issues can arise:

    Data Corruption: Simultaneous writes from multiple processes can result in data corruption, where the written data becomes mixed or garbled. This can happen if the file system interleaves or merges the write operations from different processes in an uncontrolled manner.

    Inconsistent Data: If processes are writing to different parts of the same file simultaneously, it can lead to inconsistent data. For example, if two processes write different values to the same location within the file at the same time, the final content of that location becomes unpredictable.

    File Locking and Concurrency Control: File systems often employ locking mechanisms to control access to files and ensure data integrity. These locks allow processes to acquire exclusive or shared access to a file, preventing simultaneous writes by multiple processes. Without proper coordination, conflicts may arise, and processes may contend for file locks, leading to delays and reduced performance.

To enable multiple processes to write data to the same file simultaneously, coordination mechanisms such as file locking, semaphores, or other synchronization techniques need to be employed. These mechanisms ensure that processes take turns or serialize their write operations to maintain data integrity and consistency.

However, it's worth noting that some distributed file systems or specialized I/O middleware, like ADIOS, may provide mechanisms to support parallel or concurrent writes to a file. These systems typically implement internal coordination and synchronization mechanisms to manage concurrent access safely. In such cases, the middleware handles the coordination and ensures data integrity, allowing multiple processes to write to the same file efficiently.

しかし一つのファイルに同時書き込みを許可しているふぃあるシステムもある。XFSは一つのファイルに同時書き込みを許可しているらしいのだが。そんなことできるのか？？？

gpt先生に聞いてみたよ。

Yes, XFS supports parallel I/O (Input/Output) operations. Parallel I/O allows multiple processes or threads to perform simultaneous read or write operations on a file system, which can significantly improve overall I/O performance in parallel computing environments.

XFS, as a high-performance file system, provides features and mechanisms that enable efficient parallel I/O. These include:

    Multiple Concurrent Operations: XFS allows multiple processes to perform concurrent I/O operations on the same file or across multiple files within the file system. This capability enables parallelism in I/O-intensive workloads, where multiple processes can read from or write to the file system simultaneously.

    Stripe Allocation: XFS supports the concept of striping, which involves dividing data across multiple storage devices or disks. By striping data, I/O operations can be distributed across multiple devices, enabling parallel access and increasing overall I/O throughput.

    Delayed Allocation: XFS employs a technique called delayed allocation, where the allocation of disk space is deferred until the data is actually written. This allows XFS to optimize I/O patterns and minimize disk head movement, enhancing parallel I/O performance.

    Parallel Metadata Operations: In addition to parallel data I/O, XFS also supports parallel metadata operations. Metadata refers to the information about the file system structure, file attributes, and directory entries. XFS enables multiple processes to perform concurrent metadata operations, such as file creation, deletion, or directory updates, improving overall file system performance.

By supporting parallel I/O operations, XFS can effectively utilize the resources of modern storage systems, such as parallel file systems or storage area networks (SANs), and harness the power of parallel computing to achieve high-performance data processing. It is particularly beneficial in scenarios involving large-scale simulations, data analytics, or any workload that involves concurrent access to the file system by multiple processes.

ext4は？？

The Ext4 (Fourth Extended File System) file system, which is the default file system for many Linux distributions, does not natively support parallel I/O (Input/Output) operations. Ext4 is primarily designed for single-client systems and does not provide built-in mechanisms for concurrent access to the file system by multiple processes.

In Ext4, file system operations are typically serialized, meaning that only one process can perform read or write operations on a file at a time. This design choice prioritizes data integrity and consistency over parallelism and concurrency.

However, it's important to note that while Ext4 itself does not directly support parallel I/O, the underlying I/O subsystem or storage infrastructure can still allow for parallelism in certain scenarios. For example:

    RAID Systems: If an Ext4 file system is hosted on a RAID (Redundant Array of Independent Disks) system that supports parallel I/O, such as a RAID 0 or RAID 5 configuration, it may be possible to achieve parallelism at the hardware level. The RAID system can distribute I/O operations across multiple disks, providing improved performance.

    Distributed File Systems: Ext4 can be used on top of distributed file systems, such as Lustre or GlusterFS, which are designed to support parallel access by multiple clients. In these cases, the parallelism is achieved at the distributed file system layer rather than at the Ext4 level.

    Higher-Level Middleware: Parallel I/O capabilities can also be provided by higher-level middleware or libraries, such as ADIOS (Advanced Input/Output System) or MPI-IO (Message Passing Interface I/O). These middleware solutions can facilitate parallel I/O operations by coordinating access to the file system and optimizing data movement across processes.

If parallel I/O is a crucial requirement for your specific application or workload, you may consider utilizing a file system explicitly designed for parallel computing, such as Lustre, GPFS (IBM Spectrum Scale), or BeeGFS. These file systems are specifically engineered to support parallel access and high-performance I/O in distributed and parallel computing environments.

なるほど、どのレイヤーでparallel I/Oが実装されているかっていうのも重要なのね。例えば、XFSはファイルシステムのレイヤーでparallel I/Oが実装されている。つまり、single fileに複数のプロセスが同時にアクセスできるってことだと思う。

しかし、Lustreとかのベースにext4を置くことは可能。なぜなら、Lustreは複数のファイルにまたがってparallel I/Oをするからだよね。なるほどいい勉強になる。

他にもadiosやMPI-IOとかのmiddlewareやライブラリを使って Parallel I/Oを実現することが可能だと。なるほど。これは勉強になるぞ。どういう風実現しているかも上に書いてあるよね。

Adiosがどういう風に動いているか、チョットもう一回まとめさせてくれ。

ADIOS (Advanced Input/Output System) achieves parallel I/O (Input/Output) by leveraging several techniques and strategies that enable efficient data movement and coordination across multiple processes. Here are the key ways in which ADIOS achieves parallel I/O:

1.     Aggregation and Coalescing: ADIOS allows the aggregation and coalescing of small data writes from multiple processes into larger, more efficient I/O operations. Instead of each process performing individual I/O operations, ADIOS collects data from multiple processes and writes it as a single operation. This reduces the overhead associated with frequent file system interactions and improves performance.

2. Asynchronous I/O: ADIOS employs asynchronous I/O, which decouples the I/O operations from the main simulation or computation. While the simulation continues running, ADIOS asynchronously writes the data to the storage system in the background. Asynchronous I/O enables overlapping of computation and I/O, reducing the overall time spent waiting for I/O operations to complete.


3. Data Transformations and Compression: ADIOS provides support for data transformations and compression. Data can be transformed or reshaped before writing to storage, allowing for optimized I/O operations. Additionally, ADIOS enables data compression to reduce the size of data before writing, leading to faster write operations and lower storage requirements.


4. Parallel File I/O: ADIOS is designed to take advantage of parallel file systems and storage systems. It supports parallel file I/O operations, allowing multiple processes to concurrently read from or write to different files or file regions. By distributing the I/O workload across multiple storage devices or servers, ADIOS achieves higher overall I/O throughput.


5. Collectives and Communication: ADIOS employs collective I/O operations, where multiple processes cooperate to perform I/O operations together. This coordination minimizes the number of communication calls between processes, reducing overhead and improving efficiency. ADIOS utilizes communication libraries such as MPI (Message Passing Interface) to enable efficient data exchanges and synchronization during parallel I/O operations.


6. Metadata Optimization: ADIOS optimizes metadata management to reduce the overhead associated with storing and accessing metadata. It employs techniques such as hierarchical metadata organization and metadata compression to minimize the metadata-related operations, resulting in improved I/O performance.

何となくADIOSがわかってきたぞ。まあ簡単に言うと、ファイルI/Oを最適化してくれるライブラリなわけだな。そうだろ？？そうなんだろ？？そうだといってくれ！！ファイルI/Oに限らずか。どうなんだろう、ネットワークのI/Oも最適化できるのだろうか？でも上のADIOSの説明を見るとしてくれそうだよね。

ちょっと話は変わるけど、こんな質問をGPT先生にしてみた。 Is it possible to combine adios with hdf5? ってね。

Yes, it is possible to combine ADIOS (Advanced Input/Output System) with HDF5 (Hierarchical Data Format version 5). ADIOS and HDF5 are both powerful middleware libraries for managing scientific data, and they can be used together to enhance data management and I/O capabilities.

Here are a couple of common approaches to combining ADIOS and HDF5:

    ADIOS Output to HDF5: ADIOS can be used as a data transport mechanism to efficiently write data from simulations or applications to HDF5 files. Instead of directly writing data to HDF5 using the HDF5 API, the application can use ADIOS to aggregate, compress, and transport the data to the ADIOS engine. The ADIOS engine can then be configured to output the data in HDF5 format, storing it in HDF5 files. This approach leverages the I/O optimizations and flexibility of ADIOS while utilizing the rich data organization and metadata capabilities of HDF5.

    ADIOS as a Bridge: ADIOS can serve as a bridge between applications using ADIOS for I/O and existing codes or tools that work with HDF5. In this scenario, the application writes data to ADIOS using the ADIOS API, and then ADIOS can be configured to transform and output the data in HDF5 format. This allows applications using ADIOS to seamlessly interact with tools or workflows that rely on HDF5 data, enabling interoperability between different software components.

By combining ADIOS and HDF5, users can take advantage of the strengths of both libraries. ADIOS provides efficient data movement, parallel I/O, and flexibility for large-scale simulations and high-performance computing environments. HDF5, on the other hand, offers a versatile data model, rich metadata capabilities, and extensive ecosystem of tools and libraries for data analysis and visualization.

だってさ。いいじゃない。 ちなみに、俺の研究で分散ファイルシステムを使いたかったのは結局ファイルのI/Oがボトルネックになるのを防ぎたかったから。なんかLustreを使いたかったんだけど、どうやらxfsとかノードローカルでもparallel I/Oはできるって話だね。これ結構大事かもしれないね。理解だね。