Research Focus: Week of December 16, 2024

NEW RESEARCH

NeoMem: Hardware/Software Co-Design for CXL-Native Memory Tiering

The Compute Express Link (CXL) open standard interconnect enables integration of diverse types of memory into servers via its byte-addressable SerDes links. To fully utilize CXL-based heterogeneous memory systems (which combine different types of memory with varying access speeds), it’s necessary to implement efficient memory tiering—a strategy to manage data placement across memory tiers for optimal performance. Efficiently managing these memory systems is crucial, but has been challenging due to the lack of precise and efficient tools for understanding how memory is accessed.

In a recent paper: NeoMem: Hardware/Software Co-Design for CXL-Native Memory Tiering researchers from Microsoft propose a novel solution which features a hardware/software co-design to address this problem. NeoMem offloads memory profiling functions to CXL device-side controllers, integrating a dedicated hardware unit called NeoProf, which monitors memory accesses and provides the operating system (OS) with crucial page hotness statistics and other system state information. On the OS kernel side, the researchers designed a revamped memory-tiering strategy, enabling accurate and timely hot page promotion based on NeoProf statistics. Implemented on a real FPGA-based CXL memory platform and Linux kernel v6.3, NeoMem demonstrated 32% to 67% geomean speedup over several existing memory tiering solutions.

NEW RESEARCH

Chimera: Accurate retrosynthesis prediction by ensembling models with diverse inductive biases

Planning and conducting chemical syntheses is a significant challenge in the discovery of functional small molecules, which limits the potential of generative AI for molecular inverse design. Although early machine learning-based retrosynthesis models have shown the ability to predict reasonable routes, they are less accurate for infrequent, yet important reactions.

In a recent paper: Chimera: Accurate retrosynthesis prediction by ensembling models with diverse inductive biases, researchers from Microsoft and external colleagues address this limitation, with a new framework for building highly accurate reaction models. Chimera incorporates two newly developed models, each achieving state-of-the-art performance in their respective categories. Evaluations by PhD-level organic chemists show that Chimera’s predictions are preferred for their higher quality compared to baseline models.

The researchers further validate Chimera’s robustness by applying its largest-scale model to an internal dataset from a major pharmaceutical company, demonstrating its ability to generalize effectively under distribution shifts. This new framework shows the potential to substantially accelerate the development of even more accurate and versatile reaction prediction models.

NEW RESEARCH

The GA4GH Task Execution API: Enabling Easy Multicloud Task Execution

In bioinformatics and computational biology, data analysis often involves chaining command-line programs developed by specialized teams at different institutions. These tools, which vary widely in age, software stacks, and dependencies, lack a common programming interface, which makes integration, workflow management and reproducibility challenging.

A recent article (opens in new tab) emphasizes the development, adoption and implementation of the Global Alliance for Genomics and Health (GA4GH) Task Execution Service (TES) API, created in collaboration with researchers at Microsoft and other institutions. The TES API offers a unified schema and interface for submitting and managing tasks, seamlessly bridging gaps between on-premises high-performance and high-throughput computing systems, cloud platforms, and hybrid infrastructures. Its flexibility and extensibility have already made it a critical asset for applications ranging from federated data analysis to load balancing across multi-cloud systems.

Adopted by numerous service providers and integrated into several workflow engines, TES empowers researchers to execute complex computational tasks through a single, abstracted interface. This eliminates compatibility hurdles, accelerates research timelines, reduces costs and enables “compute to data” solutions—essential for tackling the challenges of distributed data analysis.

NEW RESEARCH

RedCode: Risky Code Execution and Generation Benchmark for Code Agents

Increasing use of code agents for AI-assisted coding and software development has brought safety and security concerns, such as generating or executing malicious code, which have become significant barriers to real-world deployment of these agents.

In a recent paper: RedCode: Risky Code Execution and Generation Benchmark for Code Agents, published at NeurIPS 2024, researchers from Microsoft and external colleagues propose comprehensive and practical evaluations on the safety of code agents. RedCode is an evaluation platform with benchmarks grounded in four key principles: real interaction with systems, holistic evaluation of unsafe code generation and execution, diverse input formats, and high-quality safety scenarios and tests.

This research evaluated three agents based on various large language models (LLMs), providing insights into code agents’ vulnerabilities. For instance, results showed that agents are more likely to reject executing unsafe operations on the operating system. Unsafe operations described in natural text lead to a lower rejection rate than those in code format. Additional evaluations revealed that more capable base models and agents with stronger overall coding abilities, such as GPT-4, tend to produce more sophisticated harmful software.

These findings highlight the need for stringent safety evaluations for diverse code agents. The underlying dataset and related code are publicly available at https://github.com/AI-secure/RedCode (opens in new tab).

NEW RESEARCH

Towards industrial foundation models: Integrating large language models with industrial data intelligence

Although large language models (LLMs) excel at language-focused tasks like news writing, document summarization, customer service, and supporting virtual assistants, they can face challenges when it comes to learning and inference on numeric and structured industry data, such as tabular and time series data. To address these issues, researchers from Microsoft propose a new approach to building industrial foundation models (IFMs). As outlined in a recent blog post, they have successfully demonstrated the feasibility of cross-domain universal in-context learning on tabular data and the significant potential it could achieve.

The researchers designed Generative Tabular Learning (opens in new tab) (GTL), a new framework that integrates multi-industry zero-shot and few-shot learning capabilities into LLMs. This approach allows the models to adapt and generalize to new fields, new data, and new tasks more effectively, flexibly responding to diverse data science tasks. This technical paradigm has been open-sourced (opens in new tab) to promote broader use.