1. Workspace Acceleration and
Storage Reduction:
A Comparison of Methods &
Introduction to IC Manage Views
Roger March and Shiv Sikand, IC Manage, Inc.

2. Digital Assets Growing at Rapid Rate
File systems are being stressed by rapidly expanding digital assets. These growing datasets are
being driven by the high capacity needs of companies designing multi-function consumer
electronics and biomedical devices and software companies developing video games and
enterprise systems.
It is a world markedly different from traditional software development. In terms of scale, it is not
uncommon to see Perforce depots encompassing many terabytes in size composed of
hundreds of millions of files. A single client spec may define a workspace of many gigabytes
and tens of thousands of files. An organization may have thousands of users spread across the
globe.
Content is stored and modified on local drives by individual users working on workstations or
laptops; however, regressions, analysis, and build farms that utilize this content will typically run
on some form of networked file storage to allow large sets of machines to operate on the data
seamlessly.
A 2012 independent study of 524 respondents pointed out the top challenges associated with
network file systems, which showed the clear impact of these expanding digital assets. The top
problems were slow remote and/or local workspace syncs and storage issues such as storage
capacity not keeping up with expanding data volumes and the high cost of adding network
attached storage (NAS) devices. Additionally, the respondents cited application slowdown, with
network storage bottlenecks increasing tool iteration time by 30 percent.
File System Adaptation for Expanding Digital Assets
An optimally managed file system utilizing Perforce SCM will have key factors, even in the face
of continuously expanding digital assets.
Client workspace syncs should be rapid to encourage continuous integration and check-ins.
Network disk space usage should be optimally used such that network storage capacity doesn’t
interfere with daily execution. Directly related to this is the need to minimize network bandwidth
to avoid creating bottlenecks that slow file access and must constantly be managed. The
system must be designed to scale with a growing number of users and expanding digital assets.
Further, such any infrastructure enhancements should be compatible with existing storage
technologies and be storage vendor agnostic to allow organizations to adapt to rapidly changing
infrastructures. The enhanced system must be reliable in the event of failures or errors.
Individual users must be able to maintain workspace file control and stability, without
cumbersome, error-prone manual management of network cache storage and different
versions. And finally, development teams should be able to build workspaces anywhere and on
demand, avoiding problems and costs associated with disk space allocation.

3. High Demand on Network Storage
Beyond sheer size, the character of the IT environment has shifted over the years. It used to be
that nearly all Perforce workspaces resided on a user’s workstation. Today’s workspaces tend
to reside on NAS devices where the workspaces are network mounted to the machines
accessing the data. There are a number of advantages to this arrangement. One is that it allows
the user to be easily relocated to another machine without having to physically move the
workspace. For environments where virtualization is the norm, local workspace storage is not
utilized, and all files are stored on some form of networked storage. Some organizations also
feel that restricting the data to the storage appliance gives them better control for security. The
NAS device is usually hardware-optimized to its tasks; it provides much greater performance
and reliability than is available from a commodity server running a network file system (NFS)
such as CIFS.
Unfortunately, the cost of storage on NAS is dramatically higher than commodity storage. Even
with specialized hardware, use tends to expand to saturate the NAS device. The solution of
adding NAS units often makes cost a barrier to scaling.
Figure 1: Current baseline NFSes over-rely on network storage
As shown in Figure 1, many current baseline NFSes over-rely on network storage. They
duplicate file storage for every user workspace, utilizing precious Tier 1 storage space. The
deduplication optimization performed to address this issue is very inefficient due to continually
and rapidly changing datasets. Further, because local caching is underutilized due to the high
cost of solid-state storage, user file access often requires full network bandwidth, creating
bottlenecks that degrade file access time.
Network Bandwidth Bottlenecks
Many users may be working on a particular project in different or changing roles. This makes it
impractical or undesirable to continually tune the workspaces for the current role.
To address this, with traditional approaches, the workspace client is configured to cover the
entire project, and the user must download a complete set of project files for every workspace.
The clear drawback is that workspace syncs for large datasets can be extremely slow due to
network bandwidth bottlenecks, lock contention on the Perforce server due to long running sync
operations, and limited I/O bandwidth on the target storage device.

4. The performance impact is most severe for remote sites because the bandwidth available to the
Perforce server at the remote site is typically a fraction of that available on the local network. If a
large set of changes is submitted on the local network, doing simple syncs at the remote site
can take a long time; the entire change set must make its way down the wire—even if parts of
the change set have nothing to do with the area the user is currently working in. The Perforce
proxy architecture can mitigate this for multiple accesses to the same file by multiple users.
However, for wide and deep workspaces, the proxy consistency model results in large numbers
of queries to the master site, which tends to be latency limited on long haul networks.
Dynamic Virtual Workspaces: A Novel Approach to
Workspace Acceleration
With a dynamic virtual workspaces approach, the user is presented with a fully populated
workspace in near real time, through advanced virtualization techniques. The individual
workspaces are displayed rapidly irrespective of client complexity, the size of the view
mappings, or the total number of workspaces, which can be a limitation in some clone
technologies.
As the files are accessed by the operating system due to an application or user request, they
are delivered on demand to fast local storage caches through a server, replica, or proxy. After
the preliminary cold cache startup cost, most applications request files sequentially and the on-
demand nature of the access results in a major acceleration of workspace syncs compared with
the traditional approach of populating the entire workspace before task execution.
One element of the dynamic virtual workspace system design is that the local cache can be any
block device. The simplest form would be the unused disk space on a bare metal host.
Typically, only a small fraction of this disk space is used by the operating system and the rest is
unused. Another choice is the use of volatile memory instead of persistent storage such as
tmpfs. For environments requiring even more performance, local solid-state storage drives can
be utilized very cheaply for the cache.
The caches themselves are designed with built-in space reclamation capabilities, allowing the
cache to be set up and tuned for each individual workload. The caches are kept under quota by
automatically removing files via a least recently used (LRU) algorithm, and quota sizes can be
individually controlled, on a per-workspace basis.

5. Figure 2: Workspace acceleration is a major advantage of the dynamic virtual workspace
method
As Figure 2 illustrates, a major advantage of the dynamic virtual workspace method is the
workspace acceleration: It achieves near-instant workspace syncs for both local and remote
sites. Remote sites should be configured to use a local Perforce replica. Further, individual
workspaces can be modified and updated dynamically with independent view mappings to align
with advanced branching strategies.
Dynamic Virtual Workspaces Contrasted with Snap and
Clone
One alternate method to dynamic virtual workspaces is a “snap and clone” approach, where IT
sets up a template, takes a snapshot, and creates clones. The clones can then be made
available much faster than syncing a multitude of individual workspaces. The snap and clone
method does achieve some workspace sync acceleration over traditional NAS volumes;
however, the drawback is the way it restricts the mixing and matching of constantly changing file
sets, particularly when those changes involve many thousands of files. Further, it requires
ongoing scripting and maintenance for client workspace customization and under-utilizes local
caching, so remote sites can still face network bottlenecks because of long latencies between
the clone workspace and the master content source.
Local Caching Approach
Many companies are looking to improve NFS performance, particularly in virtualized
environments where boot storms, logout storms, and Monday morning sync storms result in
reduced productivity. The main solution used is to add solid-state storage caches, either inline
between the client and the filer for a vendor agnostic model or onboard the actual NAS device
itself.
However, SCM environments such as Perforce have a different use model compared to
unstructured data for which NAS environments are currently optimized. In the SCM model, the
repository contains the workspace files, which are then replicated to users as partial projections
of the depot. These files are then modified or used to generate a derived state from the source
files.
A local caching approach makes optimal utilization of local caching and network storage.
Network storage is used for transient storage for edited files only, with the edited files removed
once they are checked into Perforce. Modified files checked into Perforce are automatically
moved back to the local cache.

6. Figure 3: A local caching optimization approach
An advantage of this local caching optimization approach, as Figure 3 shows, is that expensive
network disk space is freed up instantly, such that network disk space utilization can typically be
reduced by a significant amount.
Intelligent File Redirection
The intelligent file redirection approach separates reads from writes, storing the reads in local
cache (see Figure 4). The modified files (writes) are automatically written to NAS, for
safekeeping.
Figure 4: The intelligent file redirection approach
This approach takes advantage of on-board speeds for reads instead of slower network
speeds—achieving typically twice the performance of network reads. Intelligent file redirection
also ensures that modified files on the NAS device are removed after they are checked into the
Perforce server, to instantly free up network disk space. The redirected reads, writes, and
removals are all done automatically without the need for manual handling.

7. Intelligent file redirection has other significant advantages when widely deployed in an
enterprise. By eliminating read traffic through redirection to local cache, the filers can be
optimized for sequential write performance, increasing throughput for write-intensive tasks and
prolonging the useful life of the filers by reducing both network and space utilization.
Real-Time Deduplication Reclaims Space for Check-Ins
A supplemental enhancement available with an intelligent file redirect approach is to
automatically purge write files checked into Perforce from the write network storage. This
instantaneous de-duplication frees up network disk space.
Advanced Content Delivery to Minimize Network Bandwidth,
Reduce Errors, and Increase I/O Availability
Many organizations have large tool or third-party content libraries and geographically distributed
sites. In many cases, this content must be synchronized to all sites to ensure that centrally
developed methodologies will work seamlessly at any location. This content tends to have the
characteristics of a very large canonical NFS mount point. Normally these directories live on the
NAS device consuming large amounts of space and file traffic because every machine needs to
access them for their tools and data. A variety of methods are used to synchronize them
between sites. These methods include block-based replication provided by most NAS vendors,
or file-based replication provided by tools such as rsync.
In many cases, large amounts of precious bandwidth between sites is consumed by this
replication even though the specific content that was part of a big push to remote sites may not
even be needed because the granularity of the push is too coarse. Additionally, replication
creates synchronization boundaries that can be time consuming to resolve for fast-changing
datasets. Server farms can also generate excessive I/O load on the filers as a series of jobs are
queued and run on a large number of hosts.
A solution to this problem is to use a single read-only workspace instead of the canonical NFS
mount point with replication. With this configuration, a single Perforce workspace is constructed
as a read-only object. Multiple DVW instances from any number of locations can connect to the
workspace sync state (have table). A single sync of the workspace will result in near
instantaneous global synchronization of the metadata. The DVW instances can either reside as
a single NFS mount point for all hosts in the farm or be configured as individual DVWs on each
host with local caching. As a result, the I/O is highly localized with on-demand granularity, and
similar workloads will benefit from warm caches on the execution hosts.
IC Manage Views: Accelerates Workspace Syncs and
Slashes Network Storage
IC Manage Views works with existing storage technologies to:
• Reduce network storage by four times through local caching techniques and real-time
de-duplication.
• Achieve near-instant syncs of fully populated workspaces through dynamic virtual
workspace technology.

8. • Deliver two times faster file access and speed up applications through automated
intelligent file redirection (see Figure 5).
Figure 5: IC Manage Views: accelerates workspace syncs and slashes network storage
In addition, IC Manage Views features:
• 100% percent compatibility with existing storage technologies; NAS agnostic.
• Scalability: savings increase with number of users and the size of databases.
• Flexibility in building workspaces on demand; development teams can build workspaces
anywhere, avoiding problems and costs associated with disk space allocation.
• Reliability: handles cache recovery in the event of failures or errors.
• Stability: designers maintain workspace file control. No manual management of network
cache storage and different versions.
Figure 6 presents some representative IC Manage Views benchmark results. In this example,
the workspace was 1 GB and 10K files.
Figure 6: IC Manage Views benchmark results for 1GB workspace and 10k files

9. IC Manage Views can dramatically lower costs associated with storage and increase
productivity through accelerated delivery of workspace content. It achieves these advantages
through dynamic virtual workspace, local caching, instant de-duplication, and intelligent file
redirection technologies.
About IC Manage
IC Manage provides IC Manage Views, which accelerates Perforce client workspace syncs and
drastically reduces the amount of storage needed to keep up with expanding software data. IC
Manage Views gives software teams the flexibility to build workspaces anywhere, avoiding
problems and costs associated with disk space allocation. IC Manage is headquartered at 2105
South Bascom Ave., Suite 120, Campbell, CA. For more information visit us at
www.icmanage.com.
Shiv Sikand, Vice President of Engineering, IC Manage, Inc.
Shiv founded IC Manage in 2003 and has been instrumental in the company achieving
technology leadership in high-performance design and IP management solutions. Prior to IC
Manage, Shiv was at Matrix Semiconductor, where he worked on the world’s first 3D memory
chips.
Shiv also worked at MIPS, where he led the development of the MIPS Circuit Checker (MCC).
While working on the MIPS processor families at SGI, Shiv created and deployed cdsp4, the
Cadence-Perforce integration, which he later open sourced. Cdsp4 provided the inspiration and
architectural testing ground for IC Manage. Shiv received his BSc and MSc degrees in physics
and electrical engineering from the University of Manchester Institute of Science and
Technology.
Roger March, Chief Technology Officer, IC Manage, Inc.
Prior to IC Manage, Roger worked at Matrix Semiconductor. He designed and helped build most
of its CAD infrastructure. He worked mainly on the physical and process side and also on
optimizing layout for manufacturability.
Roger began his career as a circuit and logic designer in the early days of microprocessors and
dynamic RAMs. While working as a designer at Data General and Zilog on long-forgotten
products like the microNova, microEclipse, and Z80K, he found himself drawn to the CAD side
to provide design tools that the marketplace was not yet offering. He wrote circuit, logic, and
fault simulators that were used to build and verify microprocessors of the day.
Roger then joined MIPS as its first CAD engineer. Here he built the infrastructure with a
combination of vendor and internal tools. This system was used to build all the MIPS
microprocessors as well as most of their system designs. He wrote module generators for chip
physical design, placement and allocation tools for board designs, test pattern generators and
coverage tools for verification, and yet another logic simulator—found to be 30 times faster than
Verilog-XL in benchmarks. After MIPS was acquired by Silicon Graphics, Roger became a
Principal Engineer at the company. Working in the microprocessor division, he worked on
problems in logic verification and timing closure. This dragged him more deeply into the realm of
design databases. He wrote several tools to help analyze and manipulate physical, logical, and

10. parasitic extraction datasets. This included work in fault simulation, static timing verification,
formal verification, physical floor planning, and physical timing optimization.