*What can be done with large, low cost, high performance memory?*

OmniTier’s MemStac system solutions enable not only better implementation of standard caching systems, but also bring traditionally-“too big” scientific problems requiring very large memory into the mainstream. Feel free to browse the following items for a sampling of the transformative capabilities enabled by our solution!

** DNA bioinformatics – UPDATED!**

**DNA bioinformatics – UPDATED!**

*De novo* genome assembly enumerates an entire DNA nucleotide sequence from a large number of much-smaller sub-sequences, without the benefit of a previously-known reference genome. High-throughput parallel sequencers can easily generate terabytes of raw data, which must be processed by an assembly algorithm. Solving this problem quickly is a problem of explosive interest in the bioinformatics field. Click on “learn more” find out how OmniTier’s CompStor product improves state-of-the-art sequencing!

A standard problem in the bioinformatics field is known as “k-mer counting.” The problem is simply stated: determine the number of times every small substring (“k-mer”) appears in a large DNA sequencing data set. The solutions to this problem are not nearly so simple. K-mer count algorithms are used for indexing and for error correction in DNA assembly. Click on “learn more” find out how OmniTier’s CompStor systems improve state-of-the-art k-mer counting!

**eCommerce website acceleration using MemStac-enabled Intel Optane(TM) technology**

**eCommerce website acceleration using MemStac-enabled Intel Optane(TM) technology**

OmniTier’s MemStac was recently featured at major industry events announcing the availability of Intel’s Optane technology. OmniTier applications expert **Ameen Aslam** demonstrates the many cost and performance advantages of a MemStac-enabled Optane system (hosted in the cloud at IBM’s Softlayer) in a webpage-server eCommerce problem.

**Large sparse matrix computation**

**Large sparse matrix computation**

Sparse matrices occur naturally in many diverse applications, from VLSI circuit analysis to synthetic aperture radar to eCommerce recommendation engines. Most tools using commonly-available compute resources can process calculations involving matrices containing billions of non-zero elements before resorting to offline memory types (such as hard drives), slowing the calculation to impractical durations. See how an optimized MemStac-based solution demonstrates singular value decomposition for 10TB matrices and beyond!