Scaling DNA data storage with nanoscale electrode wells.

Synthetic DNA is an attractive medium for long-term data storage because of its density, ease of copying, sustainability, and longevity. Recent advances have focused on the development of new encoding algorithms, automation, preservation, and sequencing technologies. Despite progress in these areas, the most challenging hurdle in deployment of DNA data storage remains the write throughput, which limits data storage capacity. We have developed the first nanoscale DNA storage writer, which we expect to scale DNA write density to 25 × 106 sequences per square centimeter, three orders of magnitude improvement over existing DNA synthesis arrays. We show confinement of DNA synthesis to an area under 1 square micrometer, parallelized over millions of nanoelectrode wells and then successfully write and decode a message in DNA. DNA synthesis on this scale will enable write throughputs to reach megabytes per second and is a key enabler to a practical DNA data storage system.

Random access in large-scale DNA data storage.

Synthetic DNA is durable and can encode digital data with high density, making it an attractive medium for data storage. However, recovering stored data on a large-scale currently requires all the DNA in a pool to be sequenced, even if only a subset of the information needs to be extracted. Here, we encode and store 35 distinct files (over 200 MB of data), in more than 13 million DNA oligonucleotides, and show that we can recover each file individually and with no errors, using a random access approach. We design and validate a large library of primers that enable individual recovery of all files stored within the DNA. We also develop an algorithm that greatly reduces the sequencing read coverage required for error-free decoding by maximizing information from all sequence reads. These advances demonstrate a viable, large-scale system for DNA data storage and retrieval.

Mapping high-fidelity volume rendering for medical imaging to CPU, GPU and many-core architectures.

Medical volumetric imaging requires high fidelity, high performance rendering algorithms. We motivate and analyze new volumetric rendering algorithms that are suited to modern parallel processing architectures. First, we describe the three major categories of volume rendering algorithms and confirm through an imaging scientist-guided evaluation that ray-casting is the most acceptable. We describe a thread- and data-parallel implementation of ray-casting that makes it amenable to key architectural trends of three modern commodity parallel architectures: multi-core, GPU, and an upcoming many-core Intel architecture code-named Larrabee. We achieve more than an order of magnitude performance improvement on a number of large 3D medical datasets. We further describe a data compression scheme that significantly reduces data-transfer overhead. This allows our approach to scale well to large numbers of Larrabee cores.