Globus Auth is a foundational identity and access management platform service designed to address unique needs of the science and engineering community. It serves to broker authentication and authorization interactions between end-users, identity providers, resource servers (services), and clients (including web, mobile, desktop, and command line applications, and other services). Globus Auth thus makes it easy, for example, for a researcher to authenticate with one credential, connect to a specific remote storage resource with another identity, and share data with colleagues based on another identity. By eliminating friction associated with the frequent need for multiple accounts, identities, credentials, and groups when using distributed cyberinfrastructure, Globus Auth streamlines the creation, integration, and use of advanced research applications and services. Globus Auth builds upon the OAuth 2 and OpenID Connect specifications to enable standards-compliant integration using existing client libraries. It supports identity federation models that enable diverse identities to be linked together, while also providing delegated access tokens via which client services can obtain short term delegated tokens to access other services. We describe the design and implementation of Globus Auth, and report on experiences integrating it with a range of research resources and services, including the JetStream cloud, XSEDE, NCAR's Research Data Archive, and FaceBase.

Data parallelism has proven to be an effective technique for high-level programming of a certain class of parallel applications, but it is not well suited to irregular parallel computations. Blelloch and others proposed nested data parallelism (NDP) as a language mechanism for programming irregular parallel applications in a declarative data-parallel style. The key to this approach is a compiler transformation that flattens the NDP computation and data structures into a form that can be executed efficiently on a wide-vector SIMD architecture. Unfortunately, this technique is ill suited to execution on today's multicore machines. We present a new technique, called data-only flattening, for the compilation of NDP, which is suitable for multicore architectures. Data-only flattening transforms nested data structures in order to expose programs to various optimizations while leaving control structures intact. We present a formal semantics of data-only flattening in a core language with a rewriting system. We demonstrate the effectiveness of this technique in the Parallel ML implementation and we report encouraging experimental results across various benchmark applications.