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Background The Expanded Disability Status Scale (EDSS), a common outcome measure in Multiple Sclerosis (MS), is obtained prospectively through a direct standardized evaluation. The objective of this study is to develop and validate an algorithm to derive EDSS scores from previous neurological clinical documentation. Methods The algorithm utilizes data from the history, review of systems, and physical exam. EDSS scores formally obtained from research patients were compared to captured EDSS (c‐EDSS) scores. To test inter‐rater reliability, a second investigator captured scores from a subset of patients. Agreement between formal and c-EDSS scores was assessed using a weighted kappa. Clinical concordance was defined as a difference of one-step in EDSS (0.5) and functional system (1.0) scores. Results Clinical documentation from 92 patients (EDSS range 0.0–8.5) was assessed. Substantial agreement between the c‐EDSS and formal EDSS (kappa 0.80; 95% CI 0.74–0.86) was observed. The mean difference between scores was 0.16. The clinical concordance was 78%. Near-perfect agreement was found between the two raters (kappa 0.89; 95% CI 0.84–0.95). The mean inter-rater difference in c-EDSS was 0.23. Conclusions This algorithm reliably captures EDSS scores retrospectively with substantial correlation with formal EDSS and high inter‐rater agreement. This algorithm may have practical implications in clinic, MS research and clinical trials.

The HPC Challenge (HPCC) Benchmark suite and the Intel MPI Benchmark (IMB) are used to compare and evaluate the combined performance of processor, memory subsystem and interconnect fabric of five leading supercomputers—SGI Altix BX2, Cray X1, Cray Opteron Cluster, Dell Xeon Cluster, and NEC SX-8. These five systems use five different networks (SGI NUMALINK4, Cray network, Myrinet, InfiniBand, and NEC IXS). The complete set of HPCC Benchmarks are run on each of these systems. Additionally, we present Intel MPI Benchmarks results to study the performance of 11 MPI communication functions on these systems.

Many large-scale parallel scientific and engineering applications, especially climate modeling, often run for lengthy periods and require data checkpointing periodically to save the state of the computation for a program restart. In addition, such applications need to write data to disks for post-processing, e.g., visualization. Both these scenarios involve a write-only pattern using Hierarchal Data Format (HDF) files. In this paper, we study the scalability of CXFS by HDF based Structured Adaptive Mesh Refinement (AMR) appli- cation for three different block sizes. The code used is a block-structured AMR hydrodynamics code that solves compressible, reactive hydrodynamic equations and characterizes physics and mathematical algorithms used in studying nuclear flashes on neutron stars and white dwarfs. The computational domain is divided into blocks distributed across the processors. Typically, a block contains 8 zones in each coordinate direction (x, y, and z) and a perimeter of guard cells (in this case, 4 zones deep) to hold information from the neighbors. We used three different block sizes of 8 × 8 × 8, 16 × 16 × 16, and 32 × 32 × 32. Results of parallel I/O bandwidths (checkpoint file and two plot files) are presented for all three-block sizes on a wide range of processor counts, ranging from 1 to 508 processors of the Co- lumbia system.