Hydropower Environmental and Industry R&D Summit 2023
The 2023 Hydropower Environmental and Industry R&D Summit will take place on April 25 from 8:30 a.m. to 4:00 p.m. EST.
PNNL Researchers Land on 2023 Highly Cited List
More than a dozen PNNL scientists have been named among the world’s most influential researchers on Clarivate’s 2023 Highly Cited Researchers List.
Scott Whalen Named 2023 PNNL Inventor of the Year
Scott Whalen, a chief scientist in the Applied Materials and Manufacturing group at PNNL, has been named 2023 PNNL Inventor of the Year
A Diagnostics Package to Evaluate Earth System Model Aerosol–Cloud Interactions with Field Campaign Measurements
The Earth System Model Aerosol–Cloud Diagnostics package version 2 uses aircraft, ship, ground, and satellite measurements to evaluate detailed physical processes in aerosols, clouds, and aerosol–cloud interactions.
Microfluidic Sample Injectors Absent Electrokinetic Injection
Microfluidic sample injection, which is based on a mechanical valve rather than electrokinetic injection into an integrated separation channel or a discrete separation column, can provide improved sample injections, enhanced capabilities, and can eliminate the need for changing the electric field in the separation channel to induce sample injection. An interface allowing the use of a discrete separation column easily allows for flexibility to utilize the microfluidic injector with existing analytical techniques. Multiple sample channels and/or sample sources can be utilized with the microfluidic sample injector.
Iterative Method Fault Injection Collection (IMIC)
Soft errors caused by transient bit flips have the potential to significantly impact an application's behavior. This has motivated the design of an array of techniques to detect, isolate, and correct soft errors using microarchitectural, archi- tectural, compilation-based, or application-level techniques to minimize their impact on the executing application. The first step toward the design of good error detection/correction techniques involves an understanding of an application's vulnerability to soft errors. To study the behavior of iterative methods in the presence of soft errors, we inject errors during the execution of these methods. In particular, we study the impact of one error (single- or multi-bit) on the execution of iterative methods. We use real life datasets from the SuiteSparse Matrix Collection (https://sparse.tamu.edu) and widely used iterative solver library (Iterative Methods Library, IML++ v1.2a). We instrument the iterative solver implementations so that our error injection methodology can control the iteration, vector, position, number of bits and position of the bits of the error injection. We employed 6 solvers and 28 datasets, performed a total of 1,744,800 error injection runs and collected more than 2.5TB data.