Accessing external resources (e.g., loading input data, checkpointing snapshots, and out-of-core processing) can have a significant impact on the performance of applications. However, no existing programming systems for high- performance computing directly manage and optimize external accesses. As a result, users must explicitly manage external accesses alongside their computation at the application level, which can result in both correctness and performance issues.

We present control replication, a technique for generating high-performance and scalable SPMD code from implicitly parallel programs. In contrast to traditional parallel programming models that require the programmer to explicitly manage threads and the communication and synchronization between them, implicitly parallel programs have sequential execution semantics and naturally avoid the pitfalls of explicitly parallel code. However, without optimizations to distribute control overhead, scalability is often poor.

We present the current status of our work towards a scalable, asynchronous many-task, in situ statistical analysis engine using the Legion runtime system, expanding upon earlier work, that was limited to a prototype implementation with a proxy mini-application as a surrogate for a full-scale scientific simulation code. In contrast, we have more recently integrated our in situ analysis engines with S3D, a full-size scientific application, and conducted numerical tests therewith on the largest computational platform currently available for DOE science applications.

This paper studies the mitigation of memory timing side channels, where attackers utilize contention within DRAM controllers to infer a victim’s secrets. Already practical, this class of channels poses an important challenge to secure computing in shared memory environments.

Existing state-of-the-art memory timing side channel mitigations have several key performance and security limitations. Prior schemes require onerous static bandwidth partitioning, extensive profiling phases, or simply fail to protect against attacks which exploit fine-grained timing and bank information.

Sparse matrix-sparse matrix multiplication (spMspM) is at the heart of a wide range of scientific and machine learning applications. spMspM is inefficient on general-purpose architectures, making accelerators attractive. However, prior spMspM accelerators use inner- or outer-product dataflows that suffer poor input or output reuse, leading to high traffic and poor performance.

It is well known that there are micro-architectural vulnerabilities that enable an attacker to use caches to exfiltrate secrets from a victim. These vulnerabilities exploit the fact that the attacker can detect cache lines that were accessed by the victim. Therefore, architects have looked at different forms of randomization to thwart the attacker’s ability to communicate using the cache.

A significant amount of specialized hardware has been developed for processing deep neural networks (DNNs) in both academia and industry. This article aims to highlight the key concepts required to evaluate and compare these DNN processors. We discuss existing challenges, such as the flexibility and scalability needed to support a wide range of neural networks, as well as design considerations for both the DNN processors and the DNN models themselves.

The miniaturization of semiconductor transistors has driven the growth in computer performance for more than 50 years. As miniaturization approaches its limits, bringing an end to Moore’s law, performance gains will need to come from software, algorithms, and hardware. We refer to these technologies as the “Top” of the computing stack to distinguish them from the traditional technologies at the “Bottom”: semiconductor physics and silicon-fabrication technology.

Software side channel attacks have become a serious concern with the recent rash of attacks on speculative processor architectures. Most attacks that have been demonstrated exploit the cache tag state as their exfiltration channel. While many existing defense mechanisms that can be implemented solely in software have been proposed, these mechanisms appear to patch specific attacks, and can be circumvented.