Category: HPC storage

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93: GreyBeards talk HPC storage with Larry Jones, Dir. Storage Prod. Mngmt. and Mark Wiertalla, Dir. Storage Prod. Mkt., at Cray, an HPE Enterprise Company

Supercomputing Conference 2019 (SC19) is coming to Denver next week and in anticipation of that show, we thought it would be a good to talk with some HPC storage group. We contacted HPE and given their recent acquisition of Cray, they offered up Larry and Mark to talk about their new ClusterStor E1000 storage system.

There are a number of components that go into Cray supercomputers and besides the ClusterStor, Larry and Mark mentioned their new SlingShot cluster interconnect which is Ethernet based with significant enhancements to congestion handling. But the call focused on ClusterStor.

What is ClusterStor

ClusterStor, is a Lustre file system hardwareappliance. Lustre has always been popular with the HPC crowd as it offered high bandwidth file services. But Lustre often took a team of (PhD) scientists to configure, deploy and run properly because of all the parameters that had to be setup for optimum performance.

Cray’s ClusterStor was designed to make configuring, deploying and running Lustre a lot simpler with a GUI and system defaults that provided an optimal running environment. But if customers still want access to all Lustre features and functionality, all the Lustre parameters can still be tweaked to personalize it.

What sort of appliance

The ClusterStore team has created a Lustre storage appliance using two systems, a 2U-24 NVMe SSD system and a 4U-106 disk drive system. Both systems use PCIe Gen 4 buses which offer 2X the bandwidth of Gen 3 and NVMe Gen 4 SSDs. Each ClusterStore E1000 appliance comes with 2 servers for HA and the storage behind it.

Larry said the 2U NVMe Gen 4 appliance offers 80GB/sec of read and 60GB/sec of write data bandwidth. And a full rack of these, could support ~2.5TB/sec of data bandwidth. One TB/sec seems like an awful lot to the GreyBeards, 2.5TB/sec, out of this world.

We asked if it supported InfiniBAND interconnects? Yes, they said it supports the latest generation of InfiniBAND but it also offers Cray’s own (SlingShot) Ethernet interconnect, unusual for HPC environments. And as in any Lustre parallel file system, servers accessing storage use Lustre client software.

ClusterStor Data Services

But on the backend, where normally one would see only LDISKFS for backend storage, ClusterStor also offers ZFS. Larry and Mark said that LDISKFS is faster but ZFS offers more functionality like snapshots and data compression.

Many of the Top 100 & Top 500 supercomputing environments are starting to deploy ML DL (machine learning-deep learning) workloads along with their normal HPC activities. But whereas HPC work has historically depended on bandwidth to read, write and move large files around, ML DL deals with small files and needs high IOPS. ClusterStor was designed to satisfy both high bandwidth and high IOPS workloads.

In previous HPC Lustre flash solutions, customers had to deal with the complexity of where to place data, such as on flash or on disk. But with net ClusterStor E1000, the system can do all this for you. That is it will move data from disk to flash when it sees an advantage to doing so and move it back again when that advantage is gone. But, just as with Lustre configuration parameters above, customers can still pre-stage data to flash.

The other challenge for HPC environments is extreme size. Cray and others are starting to see requirements for Exascale (exabyte, 10**18) byte) storage systems. In fact, Cray has a couple of ClusterStor E1000 configurations of 400PB or more already, As these systems age they may indeed grow to exceed an exabyte.

With an exabyte of data, systems need to support billions of files/inodes and better metadata services and indexing. ClusterStor offers optimized inode indexing and search to enable HPC users to quickly find the data they are looking for. Further, ClusterStor offers, data at rest encryption and supports virtual file systems, for multi-tenancy.

With a ZFS backend, ClusterStor can supply data compression and snapshots. Cray has tested ZFS compression on HPC scientific ( mostly already application compressed) data and still see ~30% reduction is storage footprint. At an exabyte of storage 30% can be a significant cost reduction

The podcast ran long, ~46 minutes. Larry and Mark had a good knowledge of the HPC storage space and were easy to talk with. Matt’s an old ZFS hand, so wanted to take even more about ZFS. I had a good time discussing ClusterStor and Lustre features/functionalit and how the HPC workloads are changing. Listen to the podcast to learn more. [The podcast was recorded on November 6th, not the 5th as mentioned in the lead in, Ed.]

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Larry Jones, Director Storage Product Management

Larry Jones is a director of storage product management for Cray, a Hewlett Packard Enterprise company.

Jones previously held senior product management roles at Seagate, DDN and Panasas.

Mark Wiertalla, Director Storage Product Marketing

Mark Wiertalla is a product marketing director for Cray, a Hewlett Packard Enterprise company.

Prior to Cray, Wiertalla held product manager roles at EMC and SGI.

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72: GreyBeards talk Computational Storage with Scott Shadley, VP Marketing NGD Systems

For this episode the GreyBeards talked with another old friend, Scott Shadley, VP Marketing, NGD Systems. As we discussed on our FMS18 wrap up show with Jim Handy, computational storage had sort of a coming out party at the show.

NGD systems started in 2013 and have  been working towards a solution that goes general availability at the end of this year. Their computational storage SSD supplies general purpose processing power sitting inside an SSD. NGD shipped their first prototypes in 2016, shipped FPGA version of their smart SSD in 2017 and already have their field upgradable, ASIC prototypes in customer hands.

NGD’s smart SSDs have a 4-core ARM processor and  run an Ubuntu Distro on 3 of them.  Essentially, anything that could be run on Ubuntu Linux, including Docker containers and Kubernetes could be run on their smart SSDs.

NGD sells standard (storage only) SSDs as well as their smart SSDs. The smart hardware is shipped with all of their SSDs, but is only enabled after customer’s purchase a software license key. They currently offer their smart SSD solutions in  America and Europe, with APAC coming later.

They offer smart SSDs in both a 2.5” and M.2 form factor. NGD Systemss are following the flash technology road map and currently offer a 16TB SSD in 2.5” FF.

How applications work on smart SSDs

They offer an open-source, SDK which creates a TCP/IP tunnel across the  NVMe bus that attaches their smart SSD. This allows the host and the SSD server to communicate and send (RPC) work back and forth between them.

A normal smart SSD work flow could be

  1. Host server writes data onto the smart SSD;
  2. Host signals the smart SSD to perform work on the data on the smartSSD;
  3. Smart SSD processes the data that has been sent to the SSD; and
  4. When smart SSD work is done, it sends a response back to the host.

I assume somewhere before #2 above, you load application software onto the device.

All the work to be done on smart SSDs could be the same for the attached SSD and the work could easily be distributed across all attached smart SSDs attached and the host processor. For example, for image processing, a host processor would write images to be processed across all the SSDs and have each perform image recognition and append tags (or other results info) metadata onto the image and then respond back to the host. Or for media transcoding, video streams could be written to a smart SSD and have it perform transcoding completely outboard.

The smart SSD processors access the data just like the host processor or could use services available in their SDK which would access the data much faster. Just about any data processing you could do on the host processor could be done outboard, on smart SSD processor elements. Scott mentioned that memory intensive applications are probably not a good fit for computational storage.

He also said that their processing (ARM) elements were specifically designed for low power operations. So although AI training and inference processing might be much faster on GPUs, their power consumption was much higher. As a result, AI training and inference processing power-performance would be better on smart SSDs.

Markets for smart SSDs?

One target market for NGD’s computational storage SSDs is hyper scalars. At FMS18, Microsoft Research published a report on running FAISS software on NGD Smart SSDs that led to a significant speedup. Scott also brought up one company they’re working with that was testing  to find out just how many 4K video  streams can be processed on a gaggle of smart SSDs. There was also talk of three letter (gov’t) organizations interested in smart SSDs to encrypt data and perform other outboard processing of (intelligence) data.

Highly distributed applications and data reminds me of a lot of HPC customers I  know. But bandwidth is also a major concern for HPC.  NVMe is fast, but there’s a limit to how many SSDs can be attached to a server.

However, with NVMeoF, NGD Systems could support a lot more “attached”  smart SSDs. Imagine a scoop of smart SSDs, all attached to a slurp of servers,  performing data intensive applications on their processing elements in a widely distributed fashion. Sounds like HPC to me.

The podcast runs ~39 minutes. Scott’s great to talk with and is very knowledgeable about the Flash/SSD industry and NGD Systems. His talk on their computational storage was mind expanding. Listen to the podcast to learn more.

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Scott Shadley, VP Marketing, NGD Systems

Scott Shadley, Storage Technologist and VP of Marketing at NGD Systems, has more than 20 years of experience with Storage and Semiconductor technology. Working at STEC he was part of the team that enabled and created the world’s first Enterprise SSDs.

He spent 17 years at Micron, most recently leading the SATA SSD product line with record-breaking revenue and growth for the company. He is active on social media, a lover of all things High Tech, enjoys educating and sharing and a self-proclaimed geek around mobile technologies.

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61: GreyBeards talk composable storage infrastructure with Taufik Ma, CEO, Attala Systems

In this episode,  we talk with Taufik Ma, CEO, Attala Systems (@AttalaSystems). Howard had met Taufik at last year’s FlashMemorySummit (FMS17) and was intrigued by their architecture which he thought was a harbinger of future trends in storage. The fact that Attala Systems was innovating with new, proprietary hardware made an interesting discussion, in its own right, from my perspective.

Taufik’s worked at startups and major hardware vendors in his past life and seems to have always been at the intersection of breakthrough solutions using hardware technology.

Attala Systems is based out of San Jose, CA.  Taufik has a class A team of executives, engineers and advisors making history again, this time in storage with JBoFs and NVMeoF.

Ray’s written about JBoF (just a bunch of flash) before (see  FaceBook moving to JBoF post). This is essentially a hardware box, filled with lots of flash storage and drive interfaces that directly connects to servers. Attala Systems storage is JBOF on steroids.

Composable Storage Infrastructure™

Essentially, their composable storage infrastructure JBOF connects with NVMeoF (NVMe over Fabric) using Ethernet to provide direct host access to  NVMe SSDs. They have implemented special purpose, proprietary hardware in the form of an FPGA, using this in a proprietary host network adapter (HNA) to support their NVMeoF storage.

Their HNA has a host side and a storage side version, both utilizing Attala Systems proprietary FPGA(s). With Attala HNAs they have implemented their own NVMeoF over UDP stack in hardware. It supports multi-path IO and highly available dual- or single-ported, NVMe SSDs in a storage shelf. They use standard RDMA capable Ethernet 25-50-100GbE (read Mellanox) switches to connect hosts to storage JBoFs.

They also support RDMA over Converged Ethernet (RoCE) NICS for additional host access. However I believe this requires host (NVMeoF) (their NVMeoY over UDP stack) software to connect to their storage.

From the host, Attala Systems storage on HNAs, looks like directly attached NVMe SSDs. Only they’re hot pluggable and physically located across an Ethernet network. In fact, Taufik mentioned that they already support VMware vSphere servers accessing Attala Systems composable storage infrastructure.

Okay on to the good stuff. Taufik said they measured their overhead and it was able to perform an IO with only an additional 5 µsec of overhead over native NVMe SSD latencies. Current NVMe SSDs operate with a response time of from 90 to 100 µsecs, and with Attala Systems Composable Storage Infrastructure, this means you should see 95 to 105 µsec response times over a JBoF(s) full of NVMe SSDs! Taufik said with Intel Optane SSD’s 10 µsec response times, they see response times at ~16 µsec (the extra µsec seems to be network switch delay)!!

Managing composable storage infrastructure

They also use a management “entity” (running on a server or as a VM),  that’s used to manage their JBoF storage and configure NVMe Namespaces (like a SCSI LUN/Volume).  Hosts use NVMe NameSpaces to access and split out the JBoF  NVMe storage space. That is, multiple Attala Systems Namespaces can be configured over a single NVMe SSD, each one corresponding to a single  (virtual to real) host NVMe SSD.

The management entity has a GUI but it just uses their RESTful APIs. They also support QoS on an IOPs or bandwidth limiting basis for Namespaces, to control manage noisy neighbors.

Attala systems architected their management system to support scale out storage. This means they could support many JBoFs in a rack and possibly multiple racks of JBoFs connected to swarms of servers. And nothing was said that would limit the number of Attala storage system JBoFs attached to a single server or under a single (dual for HA) management  entity. I thought the software may have a problem with this (e.g., 256 NVMe (NameSpaces) SSDs PCIe connected to the same server) but Taufik said this isn’t a problem for modern OS.

Taufik mentioned that with their RESTful APIs,  namespaces can be quickly created and torn down, on the fly. They envision their composable storage infrastructure to be a great complement to cloud compute and container execution environments.

For storage hardware, they use storage shelfs from OEM vendors. One recent configuration from Supermicro has hot-pluggable, dual ported, 32 NVMe slots in a 1U chasis, which at todays ~16TB capacities, is ~1/2PB of raw flash. Taufik mentioned 32TB NVMe SSDs are being worked on as we speak. Imagine that 1PB of flash NVMe SSD storage in 1U!!

The podcast runs ~47 minutes. Taufik took a while to get warmed up but once he got going, my jaw dropped away.  Listen to the podcast to learn more.

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Taufik Ma, CEO Attala Systems

Tech-savvy business executive with track record of commercializing disruptive data center technologies.  After a short stint as an engineer at Intel after college, Taufik jumped to the business side where he led a team to define Intel’s crown jewels – CPUs & chipsets – during the ascendancy of the x86 server platform.

He honed his business skills as Co-GM of Intel’s Server System BU before leaving for a storage/networking startup.  The acquisition of this startup put him into the executive team of Emulex where as SVP of product management, he grew their networking business from scratch to deliver the industry’s first million units of 10Gb Ethernet product.

These accomplishments draw from his ability to engage and acquire customers at all stages of product maturity including partners when necessary.

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56: GreyBeards talk high performance file storage with Liran Zvibel, CEO & Co-Founder, WekaIO

This month we talk high performance, cluster file systems with Liran Zvibel (@liranzvibel), CEO and Co-Founder of WekaIO, a new software defined, scale-out file system. I first heard of WekaIO when it showed up on SPEC sfs2014 with a new SWBUILD benchmark submission. They had a 60 node EC2-AWS cluster running the benchmark and achieved, at the time, the highest SWBUILD number (500) of any solution.

At the moment, WekaIO are targeting HPC and Media&Entertainment verticals for their solution and it is sold on an annual capacity subscription basis.

By the way, a Wekabyte is 2**100 bytes of storage or ~ 1 trillion exabytes (2**60).

High performance file storage

The challenges with HPC file systems is that they need to handle a large number of files, large amounts of storage with high throughput access to all this data. Where WekaIO comes into the picture is that they do all that plus can support high file IOPS. That is, they can open, read or write a high number of relatively small files at an impressive speed, with low latency. These are becoming more popular with AI-machine learning and life sciences/genomic microscopy image processing.

Most file system developers will tell you that, they can supply high throughput  OR high file IOPS but doing both is a real challenge. WekaIO’s is able to do both while at the same time supporting billions of files per directory and trillions of files in a file system.

WekaIO has support for up to 64K cluster nodes and have tested up to 4000 cluster nodes. WekaIO announced last year an OEM agreement with HPE and are starting to build out bigger clusters.

Media & Entertainment file storage requirements are mostly just high throughput with large (media) file sizes. Here WekaIO has a more competition from other cluster file systems but their ability to support extra-large data repositories with great throughput is another advantage here.

WekaIO cluster file system

WekaIO is a software defined  storage solution. And whereas many HPC cluster file systems have metadata and storage nodes. WekaIO’s cluster nodes are combined meta-data and storage nodes. So as one scale’s capacity (by adding nodes), one not only scales large file throughput (via more IO parallelism) but also scales small file IOPS (via more metadata processing capabilities). There’s also some secret sauce to their metadata sharding (if that’s the right word) that allows WekaIO to support more metadata activity as the cluster grows.

One secret to WekaIO’s ability to support both high throughput and high file IOPS lies in  their performance load balancing across the cluster. Apparently, WekaIO can be configured to constantly monitoring all cluster nodes for performance and can balance all file IO activity (data transfers and metadata services) across the cluster, to insure that no one  node is over burdened with IO.

Liran says that performance load balancing was one reason they were so successful with their EC2 AWS SPEC sfs2014 SWBUILD benchmark. One problem with AWS EC2 nodes is a lot of unpredictability in node performance. When running EC2 instances, “noisy neighbors” impact node performance.  With WekaIO’s performance load balancing running on AWS EC2 node instances, they can  just redirect IO activity around slower nodes to faster nodes that can handle the work, in real time.

WekaIO performance load balancing is a configurable option. The other alternative is for WekaIO to “cryptographically” spread the workload across all the nodes in a cluster.

WekaIO uses a host driver for Posix access to the cluster. WekaIO’s frontend also natively supports (without host driver) NFSv3, SMB3.1, HDFS and AWS S3  protocols.

WekaIO also offers configurable file system data protection that can span 100s of failure domains (racks) supporting from 4 to 16 data stripes with 2 to 4 parity stripes. Liran said this was erasure code like but wouldn’t specifically state what they are doing differently.

They also support high performance storage and inactive storage with automated tiering of inactive data to object storage through policy management.

WekaIO creates a global name space across the cluster, which can be sub-divided into one to thousands  of file systems.

Snapshoting, cloning & moving work

WekaIO also has file system snapshots (readonly) and clones (read-write) using re-direct on write methodology. After the first snapshot/clone, subsequent snapshots/clones are only differential copies.

Another feature Howard and I thought was interesting was their DR as a Service like capability. This is, using an onprem WekaIO cluster to clone a file system/directory, tiering that to an S3 storage object. Then using that S3 storage object with an AWS EC2 WekaIO cluster to import the object(s) and re-constituting that file system/directory in the cloud. Once on AWS, work can occur in the cloud and the process can be reversed to move any updates back to the onprem cluster.

This way if you had work needing more compute than available onprem, you could move the data and workload to AWS, do the work there and then move the data back down to onprem again.

WekaIO’s RtOS, network stack, & NVMeoF

WekaIO runs under Linux as a user space application. WekaIO has implemented their own  Realtime O/S (RtOS) and high performance network stack that runs in user space.

With their own network stack they have also implemented NVMeoF support for (non-RDMA) Ethernet as well as InfiniBand networks. This is probably another reason they can have such low latency file IO operations.

The podcast runs ~42 minutes. Linar has been around  data storage systems for 20 years and as a result was very knowledgeable and interesting to talk with. Liran almost qualifies as a Greybeard, if not for the fact that he was clean shaven ;/. Listen to the podcast to learn more.

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Linar Zvibel, CEO and Co-Founder, WekaIO

As Co-Founder and CEO, Mr. Liran Zvibel guides long term vision and strategy at WekaIO. Prior to creating the opportunity at WekaIO, he ran engineering at social startup and Fortune 100 organizations including Fusic, where he managed product definition, design and development for a portfolio of rich social media applications.

 

Liran also held principal architectural responsibilities for the hardware platform, clustering infrastructure and overall systems integration for XIV Storage System, acquired by IBM in 2007.

Mr. Zvibel holds a BSc.in Mathematics and Computer Science from Tel Aviv University.

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43: GreyBeards talk Tier 0 again with Yaniv Romem CTO/Founder & Josh Goldenhar VP Products of Excelero

In this episode, we talk with another next gen, Tier 0 storage provider. This time our guests are Yaniv Romem CTO/Founder  & Josh Goldenhar (@eeschwa) VP Products from Excelero, another new storage startup out of Israel.  Both Howard and I talked with Excelero at SFD12 (videos here) earlier last month in San Jose. I was very impressed with their raw performance and wrote a popular RayOnStorage blog post on their system (see my 4M IO/sec@227µsec 4KB Read… post) from our discussions during SFD12.

As we have discussed previously, Tier 0, next generation flash arrays provide very high performing storage at very low latencies with modest to non-existent advanced storage services. They are intended to replace server, direct access SSD storage with a more shared, scaleable storage solution.

In our last podcast (with E8 Storage) they sold a hardware Tier 0 appliance. As a different alternative, Excelero is a software defined, Tier 0 solution intended to be used on any commodity or off the shelf server hardware with high end networking and (low to high end) NVMe SSDs.

Indeed, what impressed me most with their 4M IO/sec, was that target storage system had almost 0 CPU utilization. (Read the post to learn how they did this). Excelero mentioned that they were able to generate high (11M random 4KB) IO/sec on  Intel Core 7, desktop-class CPU. Their one need in a storage server is plenty of PCIe lanes. They don’t even need to have dual socket storage servers, single socket CPU’s work just fine as long as the PCIe lanes are there.

Excelero software

Their intent is to bring Tier 0 capabilities out to all big storage environments. By providing a software only solution it could be easily OEMed by cluster file system vendors or HPC system vendors and generate amazing IO performance needed by their clients.

That’s also one of the reasons that they went with high end Ethernet networking rather than just Infiniband, which would have limited their market to mostly HPC environments. Excelero’s client software uses RoCE/RDMA hardware to perform IO operations with the storage server.

The other thing having little to no target storage server CPU utilization per IO operation gives them is the ability to scale up to 1000 of hosts or storage servers without reaching any storage system bottlenecks.  Another concern eliminated by minimal target server CPU utilization is that you can’t have a noisy neighbor problem, because there’s no target CPU processing to be shared.  Yet another advantage with Excelero is that bandwidth is only  limited by storage server PCIe lanes and networking.  A final advantage of their approach is that they can support any of the current and upcoming storage class memory devices supporting NVMe (e.g., Intel Optane SSDs).

The storage services they offer include RAID 0, 1 and 10 and a client side logical volume manager which supports multi-pathing. Logical volumes can span up to 128 storage servers, but can be accessed by almost any number of hosts. And there doesn’t appear to be a specific limit on the number of logical volumes you can have.

 

They support two different protocols across the 40GbE/100GbE networks. Standard NVMe over Fabric or RDDA (Excelero patented, proprietary Remote Direct Disk Array access). RDDA is what mainly provides the almost non-existent target storage server CPU utilization. But even with standard NVMe over Fabric they support low target CPU utilization. One proviso, with NVMe over Fabric, they do add shared volume functionality to support RAID device locking and additional fault tolerance capabilities.

On Excelero’s roadmap is thin provisioning, snapshots, compression and deduplication. However, they did mention that adding advanced storage functionality like this will impede performance. Currently, their distributed volume locking and configuration metadata is not normally accessed during an IO but when you add thin provisioning, snapshots and data reduction, this metadata needs to become more sophisticated and will necessitate some amount of access during and after an IO operation.

Excelero’s client software runs in Linux kernel mode client and they don’t currently support VMware or Hyper-V. But they do support KVM as a hypervisor and would be willing to support the others, if APIs were published or made available.

They also have an internal OpenStack Cinder driver but it’s not part of their OpenStack’s release yet. They’re waiting for snapshot to be available before they push this into the main code base. Ditto for Docker Engine but this is more of a beta capability today.

Excelero customer experience

One customer (NASA Ames/Moffat Field) deployed a single 2TB NVMe SSD across 128 hosts and had a single 256TB logical volume shared and accessed by all 128 hosts.

Another customer configured Excelero behind a clustered file system and was able to generate 30M randomized IO/sec at 200µsec latencies but more important, 140GB/sec of bandwidth. It turns out high bandwidth is important to many big data applications that have to roll lots of data into their analytics clusters, processing it and output results, and then do it all over again. Bandwidth limitations can impact the success of these types of applications.

By being software only they can be used in a standalone storage server or as a hyper-converged solution where applications and storage can be co-resident on the same server. As noted above, they currently support Linux O/Ss for their storage and client software and support any X86 Intel processor, any RDMA capable NIC, and any NVMe SSD.

Excelero GTM

Excelero is focused on the top 200 customers, which includes the hyper-scale providers like FaceBook, Google, Microsoft and others. But hyper-scale customers have huge software teams and really a single or few, very large/complex applications which they can create/optimize a Tier 0 storage for themselves.

It’s really the customers just below the hyper-scalar class, that have similar needs for high low latency IO/sec or high IO bandwidth (or both) but have 100s to 1000s of applications and they can’t afford to optimize them all for Tier 0 flash. If they solve sharing Tier 0 flash storage in a more general way, say as a block storage device. They can solve it for any application. And if the customer insists, they could put a clustered file system or even an object storage (who would want this) on top of this shared Tier 0 flash storage system.

These customers may currently be using NVMe SSDs within their servers as a DAS device. But with Excelero these resources can be shared across the data center. They think of themselves as a top of rack NVMe storage system.

On their website they have listed a few of their current customers and their pretty large and impressive.

NVMe competition

Aside from E8 Storage, there are few other competitors in Tier 0 storage. One recently announced a move to an NVMe flash storage solution and another killed their shipping solution. We talked about what all this means to them and their market at the end of the podcast. Suffice it to say, they’re not worried.

The podcast runs ~50 minutes. Josh and Yaniv were very knowledgeable about Tier 0, storage market dynamics and were a delight to talk with.   Listen to the podcast to learn more.

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Yaniv Romem CTO and Founder, Excelero

Yaniv Romem has been a technology evangelist at disruptive startups for the better part of 20 years. His passions are in the domains of high performance distributed computing, storage, databases and networking.
Yaniv has been a founder at several startups such as Excelero, Xeround and Picatel in these domains. He has served in CTO and VP Engineering roles for the most part.


Josh Goldenhar, Vice President Products, Excelero

Josh has been responsible for product strategy and vision at leading storage companies for over two decades. His experience puts him in a unique position to understand the needs of our customers.
Prior to joining Excelero, Josh was responsible for product strategy and management at EMC (XtremIO) and DataDirect Networks. Previous to that, his experience and passion was in large scale, systems architecture and administration with companies such as Cisco Systems. He’s been a technology leader in Linux, Unix and other OS’s for over 20 years. Josh holds a Bachelor’s degree in Psychology/Cognitive Science from the University of California, San Diego.