Andrea Miele

Thorsten Kurth, Shashank Subramanian, Peter Harrington et al.

Extreme weather amplified by climate change is causing increasingly devastating impacts across the globe. The current use of physics-based numerical weather prediction (NWP) limits accuracy due to high computational cost and strict time-to-solution limits. We report that a data-driven deep learning Earth system emulator, FourCastNet, can predict global weather and generate medium-range forecasts five orders-of-magnitude faster than NWP while approaching state-of-the-art accuracy. FourCast-Net is optimized and scales efficiently on three supercomputing systems: Selene, Perlmutter, and JUWELS Booster up to 3,808 NVIDIA A100 GPUs, attaining 140.8 petaFLOPS in mixed precision (11.9%of peak at that scale). The time-to-solution for training FourCastNet measured on JUWELS Booster on 3,072GPUs is 67.4minutes, resulting in an 80,000times faster time-to-solution relative to state-of-the-art NWP, in inference. FourCastNet produces accurate instantaneous weather predictions for a week in advance, enables enormous ensembles that better capture weather extremes, and supports higher global forecast resolutions.

Mic Bowman, Andrea Miele, Michael Steiner et al.

We present Private Data Objects (PDOs), a technology that enables mutually untrusted parties to run smart contracts over private data. PDOs result from the integration of a distributed ledger and Intel Secure Guard Extensions (SGX). In particular, contracts run off-ledger in secure enclaves using Intel SGX, which preserves data confidentiality, execution integrity and enforces data access policies (as opposed to raw data access). A distributed ledger verifies and records transactions produced by PDOs, in order to provide a single authoritative instance of such objects. This allows contracting parties to retrieve and check data related to contract and enclave instances, as well as to serialize and commit contract state updates. The design and the development of PDOs is an ongoing research effort, and open source code is available and hosted by Hyperledger Labs [5, 7].

Andrea Miele

We present a preliminary study of buffer overflow vulnerabilities in CUDA software running on GPUs. We show how an attacker can overrun a buffer to corrupt sensitive data or steer the execution flow by overwriting function pointers, e.g., manipulating the virtual table of a C++ object. In view of a potential mass market diffusion of GPU accelerated software this may be a major concern.