HDF Experiences with I/O Bottlenecks

HDF Experiences with I/O Bottlenecks
Mike Folk The HDF Group Collaborative Expedition Workshop Toward Scalable Data Management Overcoming I/O Bottlenecks in Full Data Path Processing June 10, 2008 National Science Foundation June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks
Topics What is HDF? I/O bottlenecks and HDF June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

What is HDF? June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

HDF is… A file format for managing any kind of data Software system to manage data in the format Designed for high volume or complex data Designed for every size and type of system Open format and software library, tools There are two HDF’s: HDF4 and HDF5. For simplicity we focus on HDF5. June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

HDF5 The Format June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks 5 5

An HDF5 “file” is a container…
…into which you can put your data objects. lat | lon | temp ----|-----|----- 12 | 23 | 3.1 15 | 24 | 4.2 17 | 21 | 3.6 palette June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Structures to organize objects
“Groups” “/” (root) 3-D array “/foo” lat | lon | temp ----|-----|----- 12 | 23 | 3.1 15 | 24 | 4.2 17 | 21 | 3.6 Table “Datasets” Raster image This shows that you can mix objects of different types according to your needs. Typically, there will be metadata stored with objects to indicate what type of object they are. Like HDF4, HDF5 has a grouping structure. The main difference is that every HDF5 file starts with a root group, whereas HDF4 doesn’t need any groups at all. palette Raster image 2-D array June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Everything else is built essentially from these parts.
HDF5 model Groups – provide structure among objects Datasets – where the primary data goes Data arrays Rich set of datatype options Flexible, efficient storage and I/O Attributes, for metadata Everything else is built essentially from these parts. June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

HDF5 The Software June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Tools, Applications, Libraries
HDF Software Tools, Applications, Libraries HDF I/O Library It is useful to think about HDF software in terms of layers. At the bottom layer is the HDF5 file or other data source. Above that are two layers corresponding the the HDF library. First there is a low level interface that concentrates on basic I/O: opening and closing files, reading and writing bytes, seeking, etc. HDF5 provides a public API at this level so that people can write their own drivers for reading and writing to places other than those already provided with the library. Those that are already provided include UNIX stdio, and MPI-IO. Then comes the high-level, object -specific interface. This is the API that most people who develop HDF5 applications use. This is where you create a dataset or group, read and write datasets and subsets, etc. At the top are applications, or perhaps APIs used by applications. Examples of the latter are the HDF-EOS API that supports NASA’s EOSDIS datatypes, and the DSL API that supports the ASCI data models. HDF File June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Users of HDF Software Tools & Applications HDF5 Application
Most data consumers are here. Scientific/engineering applications. Domain-specific libraries/API, tools. Tools & Applications Applications, tools use this API to create, read, write, query, etc. Power users (consumers) HDF5 Application Programming Interface Modules to adapt I/O to specific features of system, or do I/O in some special way. “Virtual file layer” (VFL) It is useful to think about HDF software in terms of layers. At the bottom layer is the HDF5 file or other data source. Above that are two layers corresponding the the HDF library. First there is a low level interface that concentrates on basic I/O: opening and closing files, reading and writing bytes, seeking, etc. HDF5 provides a public API at this level so that people can write their own drivers for reading and writing to places other than those already provided with the library. Those that are already provided include UNIX stdio, and MPI-IO. Then comes the high-level, object -specific interface. This is the API that most people who develop HDF5 applications use. This is where you create a dataset or group, read and write datasets and subsets, etc. At the top are applications, or perhaps APIs used by applications. Examples of the latter are the HDF-EOS API that supports NASA’s EOSDIS datatypes, and the DSL API that supports the ASCI data models. File system, MPI-IO, SAN, other layers “File” could be on parallel system, in memory, network, collection of files, etc. “File” June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Philosophy: a single platform with multiple uses
One general format One library, with Options to adapt I/O and storage to data needs Layers on top and below Ability to interact well with other technologies Attention to past, present, future compatibility June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Who uses HDF? June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Who uses HDF5? Applications that deal with big or complex data Over 200 different types of apps 2+million product users world-wide Academia, government agencies, industry Types of apps growing – over 200 different types of applications of HDF5 reported Uses HDF are quite varied, and include sensor data management and acquisition, archiving, image repositories and interchange, scalable computational meshes storage and retrieval on massively parallel systems, remote-sensed data access and distribution, as a container for heterogeneous collections of complex data, or as an object store for object relational databases. R&D 100 award – in 2002, “one of the 100 most technologically significant products of the year” Serious adoption and reliance on HDF5 Scientific and engineering disciplines such as physics, cosmology, medicine and meteorology rely on HDF technologies Government and quasi-government agencies using for day-to-day operations, long term preservation Next generation US civil and military weather system will use HDF5 for data distribution Aberdeen Test Center using HDF5 as object for DB of 800,000 tests Also in Europe – EU project to use HDF5 for product model data Companies like Boeing, Agilent, GE adopting for company-wide data management Some industries you wouldn’t expect – finance; film-making (Lord of the Rings) Increasing need for support, services, quick response In 2004 a company wanted to hire about half of our staff to build infrastructure for company-wide use of h5. Aberdeen Test Center needed quick porting of HDF5 Java library to 64-bit Linux Etc. June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Applications with large amounts of data
June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Large simulations A simulation can have billions of elements Each element can have dozens of associated values June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Large images Electron tomography 25-80Å resolution 4k x 4k x 500 images now 8k x 8k x 1k images soon (256 GB) June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

It’s not just about size.

Computational fluid dynamics simulation data

Earth Science (EOS) Aqua (6/01) Aura TES HRDLS MLS OMI Terra CERES MISR MODIS MOPITT Aqua CERES MODIS AMSR June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Flight test High speed, multi-stream, multi-modal data collection
Analyze and query specific parameters by time, space June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

I/O Bottlenecks and HDF

What is an I/O bottleneck?
"I/O bottleneck" – a phenomenon where the performance or capacity of an entire system is severely limited by some aspect of I/O. Two types of bottlenecks Technology – getting the data around quickly Usability/accessibility – acquiring and making use of it The role for HDF Try not to cause bottlenecks Offer ways to deal with bottlenecks when they occur What is a bottleneck? Wikipedia: The term is metaphorically derived from the neck of a bottle, where the flow speed of the liquid is limited by its neck. In engineering, bottleneck is a phenomenon where the performance or capacity of an entire system is severely limited by a single component." Hence "I/O bottleneck" refers to phenomena where the performance or capacity of an entire system is severely limited by I/O. The challenge for HDF: try not to cause an I/O bottleneck provide solutions to I/O bottlenecks June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Programming Interface File system, MPI-IO, SAN, other layers
HDF Bottlenecks Tools & Applications HDF5 Application Programming Interface Low level Interface By allowing people like HDF-EOS to build their own layers, they can put their own view on the API and what happens below. It is useful to think about HDF software in terms of layers. At the bottom layer is the HDF5 file or other data source. Above that are two layers corresponding the the HDF library. First there is a low level interface that concentrates on basic I/O: opening and closing files, reading and writing bytes, seeking, etc. HDF5 provides a public API at this level so that people can write their own drivers for reading and writing to places other than those already provided with the library. Those that are already provided include UNIX stdio, and MPI-IO. Then comes the high-level, object -specific interface. This is the API that most people who develop HDF5 applications use. This is where you create a dataset or group, read and write datasets and subsets, etc. At the top are applications, or perhaps APIs used by applications. Examples of the latter are the HDF-EOS API that supports NASA’s EOSDIS datatypes, and the DSL API that supports the ASCI data models. File system, MPI-IO, SAN, other layers File June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Sources of bottlenecks
Architectural features Characteristics of data and information objects Accessing and operating on objects Usability/accessibility – beyond specialization June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Architecture-related I/O bottlenecks
Software that does I/O often needs to operate on different systems. Differences within and among these systems can create I/O bottlenecks, as well as solutions. June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Architecture I/O bottleneck examples
Architecture Bottlenecks Not enough memory, so apps have to swap to disk In a cluster, multiple processors doing I/O on the same file simultaneously Parallel file system has special features to avoid bottlenecks HDF response Keep an HDF file in core, so I/O goes from core to core Adaptable parallel I/O strategies, such as collective I/O, merging many small accesses into one large one Implement special I/O drivers in virtual file layer to exploit parallel file systems like PVFS, GPFS, Lustre Less important to highlight traditional bottlenecks than the angles people haven’t thought about. NARA, NASA – it’s not just speed, it’s longevity, so not a bottleneck at this point in time, but not creating one in the future. (Commitment to open format, etc.) June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Characteristics of data and information objects
The size of objects, heterogeneity, and how we represent information. All are potential causes of I/O bottlenecks. June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Heterogeneity Bottlenecks Need to represent similar data from different sources, but it comes in different formats. Having to convert data for interoperability HDF responses Creation of common models and corresponding I/O libraries, avoiding need to convert Add I/O filters to auto-convert data June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Size bottlenecks Metadata/data differences: Hard to do both big I/O and small I/O efficiently, especially on high-end systems tuned for big I/O. HDF response Metadata caching options: Caches metadata & data to avoid re-reading/writing Let application control cache App can control when cache is flushed App can advise about cache sizes, replacement strategies June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Representation bottlenecks Different apps need different views of information, requiring transformation change coordinate systems ingest to database change engineering units HDF Response Group, index, reference structures provide different views at one time I/O filters can operate on data during I/O June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Accessing and operating on objects
I/O bottlenecks can occur when data is collected, generated, searched, analyzed, converted, and moved. June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Sequential R/W Bottlenecks Data from a single source at very high rate Data from multiple sources, simultaneously HDF response Use different file structures for sequential vs. random access Exploit available system optimizations (e.g. direct I/O to bypass system buffers) June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Partial access bottlenecks Access or operate on part of and object, slice through object, etc. Access to compressed object Perform a query about an object or collection HDF Response Offer rich set of partial I/O ops that recognize patterns and optimize for them Use chunking to enable fast slicing through arrays Compress in chunks, avoiding need to uncompress whole object Create and store indexes together with the data June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Remote access bottlenecks All of the above are exacerbated when the data is accessed from a distance or over a slow network HDF Response Avoid moving the data. Send operation to the data vs data to operation: Put HDF5 software inside remote data system, such as iRODS Implement remote query/access protocols, such as OPeNDAP June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Usability/accessibility Beyond specialization
Data is collected for specific purposes, then frequently turns out to have many other uses. Too often only the first users (the specialists) have the knowledge and tools to access the data and interpret it meaningfully. June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

The gaps between producer and user may be social, political, economic, semantic, temporal. The greater the gaps between producer and consumer, the greater are the challenges to usability and accessibility. June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Usability/accessibility bottlenecks
What data do I need and where do I find it? Now that I have it, what does this data really mean? Provenance? Quality? What tools do I need to access data? Do they exist? How do I use them? How do I transform the data to representations that address my information needs? How do I integrate and combine this data with my other data to create new information? Who can help me? June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

HDF responses to support usability
Layer the software to make HDF accessible at different levels of expertise Develop and promote standard models and representations in HDF (EOS, netCDF, EXPRESS) Develop and promote metadata standards and their representation in HDF. Provide simple tools to view the data Provide tools to export just the data needed to other formats. Work with tool builders, open & proprietary June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Supporting usability across time
Export to simple, enduring formats, such as XML Create maps to the data Define and store Access Information Packages Be tenacious about backward compatibility June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Philosophy: a single platform with multiple uses
One general format One library, with Options to adapt I/O and storage to data needs Layers on top and below Ability to interact well with other technologies Attention to past, present, future compatibility June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

Mike Folk mfolk@hdfgroup.org
Thank you Mike Folk June 10, 2008 Collaborative Expedition Workshop -- Overcoming I/O Bottlenecks

HDF Experiences with I/O Bottlenecks

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