FPGA Technology
Over the past decade, the rapid evolution of next generation DNA sequencing technologies has inundated the field of Bioinformatics with sequence data. As these technologies have matured, and as more and more researchers have adopted them, Data repositories and researchers alike have become flooded with sequencing data. For most researchers, this presents a significant computational burden; the solution to which is not always obvious.
For those with occasional data analysis needs, the use of Cloud computing resources may address this burden. For many however, concerns about the data transfer rate when moving data to and from the cloud persist, as do issues regarding data management and security as well as cost if you are utilizing that resource 24x7. Those with a chronic computational burden often prefer a dedicated hardware solution, which might include a CPU cluster or some alternative ‘accelerator’ hardware. CPU-clusters are commonplace as they offer great flexibility but require significant administrative overhead for maintenance & upgrades. And all that flexibility makes for sub-optimal performance if you’re doing the same sorts of things over and over again.
Other dedicated ‘accelerator’ solutions include general-purpose Graphics Processing Unit (GPU) cards or Field Programmable Gate Array (FPGA) cards. GPU cards can be conveniently programmed using standard C programming language but they lack the performance FPGA cards provide. Furthermore, porting an algorithm to run on a GPU card requires significant tuning in order to achieve optimal performance. In other words, one cannot just take standard C-code and recompile it for GPU architecture and expect a significant boost in performance. Additionally, the Bioinformatics tools that are currently available for GPU cards are all open source and lack technical support, which can also be a burden for end-users. FPGA cards offer superior performance and power efficiency as compared to CPU-clusters and GPU cards. And since the power usage associated with a standard CPU cluster can be quite large when you factor in power & cooling costs, not to mention the cost of the space devoted to those resources, FPGA-based systems can accomplish huge monetary savings in power costs alone. And System Administrators love our hardware because it is comparably easy to maintain.
Most of our TimeLogic customers utilize both FPGA and standard CPU-cluster hardware in their compute resource facilities. In these cases, the ability to offload specific applications onto one of our DeCypher® servers frees up CPU-cluster resources, which can in turn extend their lives. And upgrading or expanding one of our DeCypher® solutions is a far simpler process than upgrading a CPU-cluster.
SeqCruncher™ PCIe Accelerator Card
Designed to handle the explosion of data generated by next-generation sequencing platforms, TimeLogic's SeqCruncher™ accelerator is an entirely new circuitboard design that leverages the speed of new Xilinx® FPGA chips and the PCIe data bus. The SeqCruncher delivers 3-10X better performance compared to our previous generation FPGA accelerator card. HMM tests completed on one SeqCruncher ran 550X faster than HMMer software tests completed on one 2.66 Ghz Xeon CPU core.
In addition to hardware performance upgrades, TimeLogic has re-engineered each algorithm suite to deliver peak performance for today's search bottlenecks. The SeqCruncher architecture enables us to run Smith-Waterman searches with oligo-length queries 10-fold faster than our DeCypher® Engine G4 hardware. This is highly valuable for groups studying genomic signatures, identifying siRNA/miRNAs, mapping SNPS and short-read sequencing data to genomes.
We have completed over 400 performance tests to validate algorithm performance across multiple server types and algorithm conditions. For additional details regarding DeCypher® host-server options, please see our product FAQs page.
