White Paper: Next-Generation Genome Sequencing Using EMC Isilon Scale-Out NAS: Sizing and Performance Guidelines

White Paper

NEXT-GENERATION GENOME SEQUENCING
USING EMC ISILON SCALE-OUT NAS: SIZING
AND PERFORMANCE GUIDELINES

Abstract
This EMC Isilon Sizing and Performance Guideline white paper
reviews the Key Performance Indicators (KPIs) that most strongly
impact the production processes for Next-Generation Sequencing
(NGS) workflows.

August 2012

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Part Number H19061

Next-Generation Genome Sequencing Using EMC Isilon Scale-out NAS 2

Table of Contents
Executive summary............................................................................... 4
Introduction ........................................................................................ 4
NGS workflow – sequencing instruments and file types ............................. 5
NGS workflow – HPC ............................................................................. 7
NGS workflow – Isilon scale-out NAS .....................................................10
EMC Isilon scale-out NAS overview ........................................................12
Simple .............................................................................................. 12
Scalable............................................................................................ 13
Predictable ........................................................................................ 13
Efficient ............................................................................................ 13
Available ........................................................................................... 14
Enterprise-ready ................................................................................. 14
NGS: key performance indicators ...........................................................17
HPC server parameters ......................................................................... 17
Network infrastructure parameters........................................................... 18
Isilon storage configuration parameters .................................................... 18
Summary .......................................................................................... 19
Conclusion ..........................................................................................20


Executive summary
Next-generation sequencing (NGS) workflows are comprised of genome sequencer
instrumentation, high-performance computing (HPC) infrastructure, a network-
attached storage (NAS) platform, and the network infrastructure connecting these
components together.
Raw NGS data is the largest component of an NGS process, making data storage
capacity and scalability important factors in NGS performance. The raw TIFF image
from the sequencer can be up to 70 percent of the total dataset. These files may be
compressed and stored for later use. Most organizations do not save the TIFF images,
but retain either the BCL or FASTQ files as the raw files. Each sequencing run can also
generate analysis data in the range of 50-200 GB. With faster sequencers and larger
read lengths, this can add up to between approximately 1 PB and 2 PB per year for a
facility with three NGS sequencers.
Beyond capacity scalability, I/O performance is also a critical file storage attribute for
overall NGS performance and efficiency. NGS is I/O bound rather than processor bound,
and therefore storage I/O performance has a high impact on overall NGS performance
in relation to other NGS workflow parameters.
Internal EMC testing has determined that the Key Performance Indicators (KPIs) that
most affect the performance of NGS applications are:
 Total random access memory (RAM) size on HPC cluster nodes (recommended at
3 GB/core)
 RAM and SSD allocation on the EMC® Isilon® storage cluster – place maximum
allowable RAM on the performance layer and minimum recommended on the
archival layer with about 1 percent to 2 percent of the raw storage capacity
as SSD
 Storage configuration parameters: NFS version V4, NFS async enabled, TCP
MTU (jumbo frames), LACP (2x 1 Gb/s or 4x 1 Gb/s), and tuning the Grid
Engine package

Introduction
Over the past five years, the precision and effectiveness of sequencing technology
has considerably increased the pace of biological research and discovery. The resources
focused on molecular biology, cellular biology, and bioinformatics continue to accelerate
at a significant pace. Projections indicate that before the end of the 21st century, we
could gain a full understanding of the workings of our DNA. Such knowledge could
allow us to improve our collective quality of life through a better understanding of how
a specific genetic variation impacts a drug’s efficacy or toxicity, or, by possibly providing
the knowledge to eradicate a range of genetically based disorders.
DNA exome sequencing is an approach to selectively sequence the coding regions of
the genome as an easier yet still effective alternative to whole genome sequencing.
The exome of the human genome is formed by exons. Exons are short, functionally


important coding sequences of DNA within the gene’s mature messenger RNA that
constitute about 1.5 percent of the human genome.1
Many large-scale exome sequencing projects are underway to analyze human
diseases. This technology is often the choice as it is more affordable than whole
genome sequencing (WGS) and therefore allows analyzing more patients. In addition,
it has the advantage that resulting data volumes are much smaller and therefore
easier to handle. However, recent studies2 focused on this question found that both
technologies complement each other. As neither the whole genome nor the large-
scale exome sequencing technologies cover all sequencing variants, it is optimal to
conduct both experiments in parallel.
A single human genome—composed of a total of about 3.2 billion base pairs—requires
about 1.2 GB of unassembled storage. Industry analysts predict that the estimated
number of human whole genomes sequenced will explode from 25,000 genomes in
2012, to between 50,000 and 100,000 in 2013, and up to about 1 million by 2015.
The key enabling technology for NGS are the many commercial sequencers available
from various companies, including Illumina, Life Technologies, Roche/454 Life Sciences ,
and others. These sequencers interface to a computer network, which correlates and
concatenates the billions of overlapping segments of DNA sequence short reads that
have been streamed to or stored on a NAS system.
Accommodating the output rate of the sequencers requires a precisely designed and
balanced system. The peak rate of data (base pairs) produced by an Illumina sequencer,
for example, is already approaching 600 GigaBases per week, equivalent to about 100
whole human genomes. The range of data per year for an Illumina sequencer is from
350 TB to 1 PB.
The components of the NGS workflow are comprised of:
 Genome Sequencer instruments,
 HPC infrastructure,
 NAS platform, and the
 Network infrastructure stitching these components together.
These four components make up the hierarchy of the NGS gene-sequencing architecture.
Each component depends on the other and must have the ability to adapt and scale
to meet current and future sequencing needs. If one component creates a bottleneck,
then the performance of the entire NGS system suffers. The focus of this document
endeavors are optimum performance and sizing guidelines for the core components
of NGS: the HPC infrastructure and network-attached storage.

NGS workflow – sequencing instruments and file types
The applications at the heart of NGS data creation come from important established
and emerging organizations involved in bringing NGS to market. The list includes
software from Illumina ®, Life Technologies (Applied Biosystems), Roche/454, Ion Torrent,

1
See Gilbe rt W (February 1978). ―Why genes in pie ce s?‖. Nature 271 (5645): 501.
2
See Performance comparison of exome DNA sequencing te chnologie s. Clark MJ, C hen R , Lam HY, Karcze wsk i KJ,
Che n R , Eusk irchen G, Butte AJ, Snyde r M, Department of Ge ne tics, Stanford Unive rsity School of Medicine , Stanford, C A, USA.


Pacific Biosciences, and a myriad of open source offerings such as Galaxy. Running
these applications in a research and analysis environment places complex and special
requirements on the IT systems, and in particular, the storage infrastructure. This
document will focus on the Illumina technologies—specifically CASAVA™ for the
sequence analysis software. Other Genome Assembly and Analysis Platforms like
Galaxy would be summarized in subsequent documents.
An NGS environment typically consists of Scientific, Lab, and Analysis users:
 The Scientific User initiates the method of genome sequencing and
instrumentation. This may also be the Analysis User.
 The Lab User runs the experiment (chemistry workflow) using a multiplexed
sampling scheme (or lanes) supported by the NGS instrument.
 The Analysis User works on the results from the genome sequencing study with
bioinformatics tools and algorithms.
Most commercial NGS data centers also have a trained storage administrator on their
staff. With the growing use of NGS technologies, a new user has emerged for these
storage systems. The scientist or researcher running the experiments frequently
handles the data directly. Data management has to be intuitive to allow this new user
to run experiments and administer the data with minimal difficulty. In addition, the
storage administrator needs access to the more advanced management features to
set sophisticated management policies. These help with optimization of performance
and use of storage system. It is important that the storage system deployed provide
management capabilities tuned to both types of users.
A graphical representation of the typical NGS data flow is shown below in Figure 1:

Figure 1. NGS architecture, data flow, and file types

The results stage of the NGS workflow as shown in Figure 1 consists of a number of
successive steps each involving file conversions and each resulting in approximately
5x smaller file sizes. These steps include conversion of the raw image file into base-
call data, then of base call data into FASTQ text-based file format for storing both


biological sequence and its corresponding quality scores, for example, using LQUAL
or QUAL formats. This is followed by conversion into BAM (Binary Alignment Map) file
data followed by conversion into Variant Calls (VCF) file data, which is converted next
into results data in SRA format. This tertiary file data is typically kept forever, needs
to be kept safe, as well as available, and accumulates over time.
Today’s instruments produce higher level information and may avoid some of the
intermediate steps, thus reducing output data compared to previous NGS systems.
Therefore, data flows generated by the latest NGS instruments have typically decreased
in size per run. This dec rease has been offset by a larger number of experiments,
secondary data, and increased consumption by users working downstream on many
different efforts and workflows. The size and characteristics of data produced from
these efforts place unpredictable demands on capacity as well as on throughput of the
storage systems. NGS storage environments need to be able to adapt to demands for
more capacity from post-processing work done by researchers downstream from the
first data capture.

NGS workflow – HPC
NGS applications have both common and unique analysis tools. All applications generate
large files that must be managed through multiple rounds of processing. Although many
tools were written specifically for easy implementation on a high-end desktop computer
(e.g., 64-bit dual- or quad-core, 16 GB RAM), routine analysis is typically conducted
on high-performance compute clusters.
Using a high-performance compute cluster, secondary analysis processing can generally
be done at a rate equal to or faster than primary data generation. Due to the open-
ended nature of tertiary analysis, a similar rate estimate cannot be precisely stated.
It is important that the parallelization of the NGS analysis platform be well understood
before planning on optimum server CPU core sizing. Most of the NGS tools are at least
multi-processor aware or are highly parallelized by simply dividing the sequence data,
the assembly algorithm, variant calling, or all, and starting separate analysis on these
data subsets. For NGS applications, the current parallelization per process is typically
between 75 percent and 90 percent.
As genomics has very large, semi-structured, file-based data and is modeled on post-
process streaming data access and I/O patterns that can be parallelized, it is ideally
suited for the Hadoop software framework3 which consists of two main components:
a file system and a compute system—the Hadoop Distributed File System (HDFS)
and the MapReduce framework, respectively.

3
See Hadoop in the life scie nces: an Isilon Systems white pape r. Joshi S.


Figure 2. Amdahl’s Law and parallelization

One of the basic tenets in HPC, Amdahl’s Law4, postulates that adding more
microprocessor cores to a process does not speed it up linearly. A 64 core HPC
platform is estimated to be the performance threshold for 75 percent parallelization
per NGS process, which delivers a speedup of 4x (see Figure 2). Even more than
100 cores per active NGS process do not speed up the process substantially when the
algorithm(s) are between 75 percent and 90 percent parallelization. During actual
testing of the NGS processes in the range of 75-90 percent parallelization, the
speedup from 12 cores to 72 cores was found to be only about 1.25x.
Horizontal platforms like Hadoop that combine compute and data in a parallel
context would benefit genome assembly considerably.

4
See ―Validity of the single proce ssor approach to achie ving large -scale computing capabilities‖, Amdahl G, AFIPS Conference
Proceedings (30): 483–485, 1967


Figure 3. Performance curves for NGS using Illumina CASAVA

As shown in Figure 3 above, the NGS process is storage I/O and memory bound. The
performance curves show a direct relationship between NGS performance and saturation
of read/write I/O and memory functionality. In contrast, there is an inverse relationship
between the CPU core utilization and storage I/O and memory functionality. This number
may be due to mutual dependencies or portions of the process that can only be performed
sequentially; NGS algorithms requiring movement of large amounts of data in and out
of the CPU; startup overhead including base calling and other large numbers of small
file writes; and degree of serialization involved in communication.
In view of the above discussion, it is recommended that the HPC server hardware
platform be configured with:
 Best I/O chipset, for example, using the latest generation Intel I/O controllers
 Highest DRAM speed (with a minimum of 3 GB per core of RAM)
 Multi-core CPU set with > 2 GHz processors
 Simplified BIOS and driver upgrades with a single management console for all
driver upgrades
 Linux driver compatibility (over 90 percent of all HPC systems are Linux-based)
 Disk drives between 200 GB and 600 GB with RAID 10
 Cluster management tools such as Ganglia
Increasing the network bandwidth up to 4 Gbps would alleviate the read I/O and
memory saturation.


NGS workflow – Isilon scale-out NAS

Figure 4. Data flow using Illumina NGS process

NGS production processes generate potentially millions of files with terabytes of
aggregate storage impacting the capacity and manageability limits of existing file
server structures.
Figure 4 shows the data flow including a file number and capacity summary of an actual
NGS process using Illumina sequencer and Isilon scale-out NAS storage. As can be seen,
the process generates over 500,000 having aggregate size of greater than 5 TB over
the course of the 48-hour run.
Raw NGS data is the largest component of an NGS process. The raw TIFF image can be
up to 70 percent of the total dataset. These files may be compressed and stored for
later use. Most organizations do not save the TIFF images, but retain either the BCL
or FASTQ files. If sequencing as a service is used, the input to the process is a BAM
file. Each sequencing run can also generate intermediate and final analysis data in the


range of 50-200 GB. With faster sequencers and larger read lengths, this can add up to
between 1 PB and 2 PB per year for a facility with three NGS sequencers.
Genomics is a data reduction process from the raw instrument information (images or
voltages) to the variants. This reduction process follows the ―Rule of One-Fifth‖ as
shown in the sizing table below:

File format Size, GB Illumina Size, GB IonTorrent Comments

TIFF, 2500 750 TIFF Range: 2.5 to 4 TB, Ion
WELLS Torrent™ is WELLS voltage format

BCL / SFF 500 500 Ion Torrent uses SFF

BA M 100 100 2x compression (~200 GB normal)

VCF 20 20 Variant calls

SRA, EMR 4 4 EMR (Electronic Medical Record)
includes Radiology and
Pathology images

Table 1. Data reduction for the NGS process; human whole genome, all file
sizes are approximate

Raw instrument data typically consists of large image files (2-5 TB per run are the norm),
usually in TIFF format or an electropherogram file format native to a sequencer (for
example, the SEQ format native to the Illumina sequencer). These files are only kept
long enough (7-10 days) to verify that the experiment worked. The image file for the
experiment is usually the largest file size in NGS.
Intermediate or secondary data consists of raw data processed into information of
increasing value, stored for medium- to long-term storage (1 year or more), requires
high bandwidth access for fast analysis, and is expensive to re-create, so storage needs
to be highly available. These include files in BCL format for base calling and conversion
with an aggregate ratio of approximately one-fifth compared to raw instrument data.
Beyond capacity scalability, I/O performance is also a critical file storage attribute for
overall NGS performance and efficiency. As discussed earlier, NGS is I/O bound, rather
than processor bound, and thus storage I/O performance has a high impact on overall
NGS performance in relation to other NGS workflow parameters. As a result, NGS
environments require a file storage infrastructure that is purpose-built to address the
capacity and performance scalability, efficiency, availability, and manageability challenges
of next-generation NGS environments.


EMC Isilon scale-out NAS overview
NGS is an unstructured file-based process, not a block-based storage process. The EMC
Isilon scale-out NAS manages unstructured file data through a single namespace through
its storage appliance nodes arranged in clusters which support massive scalability.
A short description of the EMC Isilon storage solution and the EMC Isilon OneFS® file
operating system with each of its features summarized below confirms its suitability
for next-generation genomic sequencing:

Simple
OneFS combines the three layers of traditional storage architectures—the file system,
volume manager, and RAID/data protection—into one unified software layer, creating
a single intelligent distributed file system that runs on an Isilon storage cluster.

Figure 5: OneFS eliminates the need for complex file management

This scale-out hardware provides the appliance on which OneFS distributed file system
resides. A single EMC Isilon cluster consists of multiple storage nodes, which are rack-
mountable enterprise appliances containing memory, CPU, networking, NVRAM, storage
media and the InfiniBand back-end network that connects the nodes together. Hardware
components are best-of-breed and benefit from ever-improving cost and efficiency
curves. OneFS allows nodes to be added or removed from the cluster at will and at any
time, abstracting the data and applications away from the hardware. Adding nodes —
instead of adding volumes and LUNS via physical disks—becomes an extremely simple
task at the petabyte (PB) scale, which is common in NGS.


Scalable

Figure 6: Linear scalability with OneFS

EMC Isilon provides a high-performance, fully symmetric cluster-based distributed
storage platform. It has linear scalability with increasing capacity—from 18 TB to
15.5 PB in a single filesystem—as compared to traditional storage. The concept of
node-based capacity growth with linear scaling is critical to NGS where scale needs to
be painless, since the process can generate upwards of 8 TB per week per instrument.
The researchers and clinicians need to focus on managing scientific data and patients,
not managing storage.

Predictable
Along with raw scaling of capacity, balancing of the content across the new nodes needs
to be predictable for an NGS workflow due to its sustained throughput requirement.
Since the instrument end keeps changing with newer technologies faster than the
HPC or Storage, this balancing and scale become invaluable. Dynamic content
balancing is performed as nodes are added or data capacity changes. There is no
added management time for the administrator, or increased complexity within the
storage system. The storage reporting application, InsightIQ, can be used to plan the
growth of a system from storage statistics bot h for infrastructure and for budgeting.

Efficient
Operational Expenditure (Opex) hinges upon efficiency, specifically in NGS, since the
total storage can run into PBs. A recent survey conducted by Scripps Institute concluded
that more than 35 percent of institutions today are at petabyte scale in NGS with a
10 percent year-over-year growth.
Isilon scale-out NAS offers an 80 percent efficiency ratio and ―smart pooling‖ of the
data across multiple tiers, making dynamic, rule-based data transfer between storage
pools an integral piece of the NGS process. This efficiency is at the application level
and tiered by the performance types:
 S-Series node for high performance (I/O per second)
 X-Series node for high throughput
 NL-Series node for archive


Figure 7: Storage tiering based on node type

The tiers in the storage cluster as shown in Figure 7 above are identified as ―pools‖
and managed by the EMC Isilon SmartPools™ application. A pool is a group of similar
nodes which is defined by the user and is based on the functionality or workflow. A pool
is governed by policies which can be changed based on needs; default policies are
built in. Policies defined by any standard file metadata: file type, size, name, location,
owner, age, last accessed, etc. Data can be migrated from pool to pool. The timing for
this data movement is configurable: default is 1x/day @ 10 PM.

Available
Data availability and redundancy are the core requirements of the scientific and clinical
staff in NGS. As NGS moves into the clinical realm, availability becomes even more
important. Flexible data protection occurs during power loss, node or disk failures,
loss of quorum, and storage rebuild. OneFS avoids the use of hot spare drives, and
simply borrows from the available free space in the system in order to recover from
failures; this technique is called virtual hot spare.
Since all data, metadata, and parity information is distributed across the nodes of the
cluster, the Isilon cluster does not require a dedicated parity node or drive, or a dedicated
device or set of devices to manage metadata. This helps to ensure that no one node
can become a single point of failure and makes the cluster ―self-healing.‖

Enterprise-ready
The NGS data system does not exist as an island; it usually coexists with other storage
and IT systems. The standard protocols that OneFS supports build the standards-based
protocol bridges to other information systems from NGS. Specifically, connectivity to the
Isilon scale-out NAS cluster is via standard file protocols: CIFS, SMB, NFS, FTP/HTTP,
iSCSI, and HDFS. The complete data lifecycle is accessible to the centralized IT group.
Snapshots, Replication, and Quotas are supported via a simple web-based UI.
Data is given infinite longevity and future-proofs the enterprise from evolving hardware
generations—eliminating the cost and pain of data migrations and hardware refreshes.


Active Directory (AD), LDAP, NIS and local users are standardized authentication and
access control available at scale. Simultaneous or rolling upgrades to OneFS are possible,
with little or no impact to the production environment.

Figure 8: Standard protocols are critical to enterprises

The software to manage OneFS is automated to eliminate complexity, as shown in
Figure 9 below:

Figure 9: OneFS software management suite

All of the applications shown above are available as software licenses and are web-based
through the main administrative user interface. A comprehensive command-line based
administration interface is also available.


OneFS Software Management Suite
Making data management easier for NGS

OneFS infrastructure software solutions meet critical data protection, access,
management, and availability needs

Application Category What it does

SmartPools Resource Implements a highly efficient, automated
management tie red storage strategy to optim ize
storage pe rformance and costs

SmartConnect Data access Enables client conne ction load balancing
and dynamic NFS failove r and failback of
client conne ctions across storage nodes to
optimize use of cluste r resources

SnapshotIQ Data prote ction Prote cts data efficiently and re liably
with se cure, near-instantaneous
snapshots while incurring little to
no pe rformance ove rhead

InsightIQ Pe rformance Max im izes pe rformance of your
management Isilon scale-out storage system with
innovative pe rformance -monitoring
and re porting tools

SmartQuotas Data Assigns and manages quotas that
management partition storage into easily managed
segments at the cluste r, dire ctory,
sub-dire ctory, use r, and group le ve ls

SyncIQ Data re plication Replicates and distributes large , m ission -
critical data se ts to multiple shared
storage systems in m ultiple sites for
re liable disaste r re cove ry capability

Table 2. Functional overview of the OneFS software suite


NGS: key performance indicators
As discussed in the HPC section, performance of the NGS process is highly dependent
on the I/O performance of the file storage system and memory resources available in the
NGS architecture. In addition, there are a range of second-order factors that need to be
considered in terms of optimizing performance for a specific NGS process, including5:
 How much faster can a given problem be solved with multiple workers (or server
cores) instead of one?
 How much more work can be done with multiple workers (or server cores) instead
of one?
 What impact do the communication requirements of the parallel NGS application
have on overall performance and scalability?
 What fraction of the resources in an NGS configuration is actually used productively
for solving the NGS problem?
The KPI for NGS consists of factors that can be used to predict and optimize the
performance of an NGS configuration and can be broken down into four categories:
 HPC server attributes: RAID, number of processor cores per HPC node, total RAM
size per HPC node
 NGS network infrastructure: TCP MTU, Channel Bonding, DNS
 Sun Grid Engine parameters: number of nodes, PAR_EXECD_INST_COUNT
 Isilon file storage attributes:
 SSD size and RAM
 NFS protocol parameters: NFS server OS, async, number of threads, locks
 Software RAID, maximum number of directories at a level, maximum number
of files in a directory, number of files less than 8 KB

HPC server parameters
RAID: With modern multi-core CPUs, the performance of software RAID is very
close to that of hardware RAID. RAID 10 (first mirroring, then striping the mirrors)
is recommended for the HPC nodes with a minimum of two identical drives per node
where both drives are bootable. The benefit of such a configuration is that the server
continues to boot seamlessly even in the face of a failure of a single drive.
Total processor cores: The empirical rule-of-thumb for total number of threads and
processes running in parallel is determined by the equation:
∑ (threads + processes) = (2 x total cores) + 1
Please note that the total number of threads include functions such as NFS and HPC
queuing as well as the processes that run NGS algorithms; it is important to document
all the processes that are multi-threaded. Amdahl’s law vis-à-vis parallelization is also
an important consideration.

5
See Introduction to High Performance Computing for Scientists and Engineers , Hage r G, Welle in G,
© 2010 Taylor & Francis Group, LLC


Total RAM size: NGS analysis requires large file processing, including functions
related to string processing, clustering of large files, and statistical quality measures,
and thus easily becomes memory-bound. As a result, a large DDR3-based RAM pool
is optimal.

Network infrastructure parameters
TCP MTU: The default Maximum Transmission Unit (MTU) (or frame size) of current
Ethernet systems is 1500 B. However, higher bandwidth network infrastructures can
handle a much higher MTU of 9000 B (called ―jumbo frames‖) for efficient data transfer.
Please note that the jumbo frame setting needs to be completed both on the HPC server
node(s) and the switch(es).
Ethernet Bonding (LACP): Ethernet Bonding using the Link Aggregation Control
Protocol (LACP) is a method used to alleviate bandwidth limitations and port-cable-
port failure issues. By combining several Ethernet interfaces to a virtual ―bond‖ interface,
the network bandwidth can be increased since LACP splits the communications and
sends frames among all the Ethernet links. Bonding 2x 1 GbE interfaces provides the
required bandwidth between HPC server nodes and NAS file storage.

Isilon storage configuration parameters
NFS Master OS: By default, EMC Isilon OneFS operating system is the NFS server. It
is recommended that this default be maintained since SmartConnect and other OneFS
features may be affected if the HPC master node OS is chosen as the NFS server.
NFS V4: NFS V4 provides improved performance, security, and robustness vis-à-vis
NFS V3. These include support of multiple operations per RPC operation (vs. a single
operation per RPC in NFS V3), use of Kerberos and access control lists (ACLs) for
security (vs. UNIX file permissions in NFS V3), use of TCP transport (vs. UDP in NFS
V3), and integrated file locking (vs. use of the adjunct Network Lock Manager protocol
for NFS V3). As a result, it is recommended that sites utilize NFS V4 for the NGS
environments. Please note that initial setting up of NFSv4 can be cumbersome.
NFS async: The NFS async (asynchronous) mode allows the server to reply to client
requests as soon as it has processed the request and handed it off to the local file
system, without waiting for the data to be written to stable storage. However, write
performance is better when synchronous mode is used (also called ‘noasync’), especially
for smaller file sizes. This is the recommended mode, especially since NFSv4 uses
TCP connectivity.
NFS number of threads: This is the number of NFS server daemon threads that are
started when the system boots. T he OneFS NFS server usually has 16 threads as its
default setting; this value can be changed via the Command Line Interface (CLI):
isi_sysctl_cluster sysctl vfs.nfsrv.rpc.[minthreads,maxthreads]
Increasing the number of NFS daemon threads improves response minimally; the
maximum number of NFS threads need to be limited to 64.
NFS ACL: The NFS ACL (Access Control List) for NFSv4 is a list of permissions associated
with a set of files or directories which contain one or more Access Control Entries (ACEs).
There are four types of ACEs: Allow, Deny, Audit, and Alarm; with three kinds of
flags: group, inheritance, and administrative. There are 13 file permissions and


14 directory permissions. OneFS manages NFS ACLs which need to be mapped to
the NFS client using the idmapd configuration.
NFS locks: The mounting and locking processes have been enhanced in NFSv4 which
supports mandatory as well as advisory locking. Caching and open delegation provide
performance improvements in most situations. More information about state is stored
on the servers in the HPC tier, enabling recovery of the files when they are in use. 6,7
Maximum number of directories at a level and files within a directory : While
Isilon OneFS supports an upper bound of 100,000 files in a directory as well as number
of directories at a level, in order to ensure highest performance while traversing a
directory tree, the maximum number of directories at a level and the maximum number
of files within a directory needs to be below 10,000.
Number of small (<8 KB) files: Random-write operations on small files have low
response times and can degrade overall application performance. In order to optimize
performance, it is recommended that Base Call files that are typically <8 KB be
aggregated into 128 KB or larger ZIP archive files.
SGE number of nodes: The Sun Grid Engine (SGE) package is a popular distributed
resource manager (DRM) and scheduler package for controlling access to and control
of cluster resources. It is recommended that at least a minimum of three SGE nodes be
used for NGS for performance and backup reasons. While a commercial version of
SGE available from Oracle, SGE is also available as open source. Other popular open
source DRM packages are Torque/Maui and Lava.
Execution daemons: The SGE PAR_EXECD_INST_COUNT variable contained within
the SGE configuration file defines the number of parallel execd (execution daemons)
for the NGS HPC cluster.
DNS location: If the HPC NGS system is run within a private network, it is
recommended that Linux BIND be installed on the HPC master node with DNS
forwarding to the organization’s DNS server.

Summary
Internal EMC testing determined that the KPIs that affect the performance the most are:
 RAM on HPC cluster server nodes (recommended at 3 GB/core)
 RAM and SSD on the Isilon storage cluster—maximum allowable RAM on the
performance layer and minimum recommended on the archival layer with about
1 percent to 2 percent of the raw storage capacity as SSD
 Storage configuration parameters: NFS version V4, NFS async enabled, TCP MTU
(jumbo frames), LACP and the Grid Engine package

6
See info on Isilon SmartLock : http://www.em c.com /collate ral/software /white-pape rs/h8325-wp-isd-smartlock.pdf
7
See info on Isilon high-pe rformance computing: http://www.isilon.com/high-pe rformance-computing


Conclusion
NGS production processes generate potentially millions of files with terabytes of
aggregate storage impacting the capacity and manageability limits of existing file
server structures. Raw instrument data typically consists of large image files (2-5 TB
per run are the norm), usually in TIFF format. The image file for the experiment is
usually the largest file size in NGS.
Genomics is a data reduction process from the raw instrument information (images or
voltages) to the variants which follows the ―Rule of One-Fifth.‖ Intermediate or
secondary data consists of raw data files including files in BCL format for base calling and
conversion have an aggregate ratio of approximately one-fifth compared to raw
instrument data.
Internal EMC testing has determined that the KPIs that affect the performance of
NGS applications the most are: total RAM size on HPC cluster nodes (recommended
at 3 Gb/core, RAM and SSD on the Isilon storage cluster [typically 1 percent of RAM
storage]), and storage configuration parameters with NFS version V4, NFS async
enabled, TCP MTU (jumbo frames), LACP (2x 1 Gb/s or 4x 1 Gb/s) and a Grid
Engine package.
NGS environments require a file storage infrastructure that is purpose-built to address
the capacity and performance scalability, efficiency, availability, and manageability
challenges of next-generation NGS applications. Cumulative network bandwidth between
HPC and NAS increases with the total number of Isilon nodes on the storage cluster.
Isilon scale-out NAS presents a range of benefits optimal for NGS. The Isilon approach
of enabling storage I/O and capacity growth through addition of cluster nodes is optimal
since NGS requires storage performance and capacity scalability to be implemented
as seamlessly as possible. In addition, dynamic content balancing performed within
Isilon scale-out NAS as nodes are added or data capac ity changes is ideal for an NGS
workflow due to its sustained throughput requirement.
Isilon scale-out NAS also offers an 80 percent efficiency ratio and ―smart pooling‖ of
the data across multiple performance tiers, making dynamic, rule-based data transfer
between storage pools an integral piece of the NGS process. Flexible, multi-dimensional
data protection which occurs within Isilon scale-out NAS during power loss, node or disk
failures, loss of quorum, and storage rebuild enables non-stop data availability for NGS.


White Paper: Next-Generation Genome Sequencing Using EMC Isilon Scale-Out NAS: Sizing and Performance Guidelines

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White Paper: Next-Generation Genome Sequencing Using EMC Isilon Scale-Out NAS: Sizing and Performance Guidelines