Intensity modulated treatment plan optimization is a computationally expensive task. The feasibility of advanced applications in intensity modulated radiation therapy as every day treatment planning, frequent re-planning for adaptive radiation therapy and large-scale planning research severely depends on the runtime of the plan optimization implementation. Modern computational systems are built as parallel architectures to yield high performance. The use of GPUs, as one class of parallel systems, has become very popular in the field of medical physics. In contrast we utilize the multi-core central processing unit (CPU), which is the heart of every modern computer and does not have to be purchased additionally. In this work we present an ultra-fast, high precision implementation of the inverse plan optimization problem using a quasi-Newton method on pre-calculated dose influence data sets. We redefined the classical optimization algorithm to achieve a minimal runtime and high scalability on CPUs. Using the proposed methods in this work, a total plan optimization process can be carried out in only a few seconds on a low-cost CPU-based desktop computer at clinical resolution and quality. We have shown that our implementation uses the CPU hardware resources efficiently with runtimes comparable to GPU implementations, at lower costs.

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Deduplication is a storage saving technique that is highly successful in enterprise backup environments. On a file system, a single data block might be stored multiple times across different files, for example, multiple versions of a file might exist that are mostly identical. With deduplication, this data replication is localized and redundancy is removed – by storing data just\ud once, all files that use identical regions refer to the same unique data. The most common approach splits file data into chunks\ud and calculates a cryptographic fingerprint for each chunk. By checking if the fingerprint has already been stored, a chunk is classified as redundant or unique. Only unique chunks are stored. This paper presents the first study on the potential of data deduplication in HPC centers, which belong to the most demanding storage producers. We have quantitatively assessed this potential for capacity reduction for 4 data centers (BSC, DKRZ,\ud RENCI, RWTH). In contrast to previous deduplication studies focusing mostly on backup data, we have analyzed over one PB\ud (1212 TB) of online file system data. The evaluation shows that typically 20% to 30% of this online data can be removed by applying data deduplication techniques, peaking up to 70% for some data sets. This reduction can only be achieved by a subfile deduplication approach, while approaches based on whole-file\ud comparisons only lead to small capacity savings.Peer ReviewedPostprint (published version

While the amount of data used by today's high-performance computing (HPC) codes is huge, HPC users have not broadly adopted data compression techniques, apparently because of a fear that compression will either unacceptably degrade data quality or that compression will be too slow to be worth the effort. In this paper, we examine the effects of three lossy compression methods (GRIB2 encoding, GRIB2 using JPEG 2000 and LZMA, and the commercial Samplify APAX algorithm) on decompressed data quality, compression ratio, and processing time. A careful evaluation of selected lossy and lossless compression methods is conducted, assessing their influence on data quality, storage requirements and performance. The differences between input and decoded datasets are described and compared for the GRIB2 and APAX compression methods. Performance is measured using the compressed file sizes and the time spent on compression and decompression. Test data consists both of 9 synthetic data exposing compression behavior and 123 climate variables output from a climate model. The benefits of lossy compression for HPC systems are described and are related to our findings on data quality.

RNA interference (RNAi) has emerged as a powerful technique for studying loss‐of‐function phenotypes by specific down‐regulation of gene expression, allowing the investigation of virus‐host interactions by large‐scale high‐throughput RNAi screens. Here we present a robust and sensitive small interfering RNA screening platform consisting of an experimental setup, single‐cell image and statistical analysis as well as bioinformatics. The workflow has been established to elucidate host gene functions exploited by viruses, monitoring both suppression and enhancement of viral replication simultaneously by fluorescence microscopy. The platform comprises a two‐stage procedure in which potential host factors are first identified in a primary screen and afterwards re‐tested in a validation screen to confirm true positive hits. Subsequent bioinformatics allows the identification of cellular genes participating in metabolic pathways and cellular networks utilised by viruses for efficient infection. Our workflow has been used to investigate host factor usage by the human immunodeficiency virus‐1 (HIV‐1), but can also be adapted to other viruses. Importantly, we expect that the description of the platform will guide further screening approaches for virus‐host interactions. The ViroQuant‐CellNetworks RNAi Screening core facility is an integral part of the recently founded BioQuant centre for systems biology at the University of Heidelberg and will provide service to external users in the near future.