arXiv subjects

Ulrich Drepper

Publications and source records attributed to Ulrich Drepper.

3 recordsLinked to original sources

arXiv2025

VeBPF Many-Core Architecture for Network Functions in FPGA-based SmartNICs and IoT

FPGA-based SmartNICs and IoT devices integrating soft-processors for network function execution have emerged to address the limited hardware reconfigurability of DPUs and MCUs. However, existing FPGA-based solutions lack a highly configurable many-core architecture specialized for network packet processing. This work presents VeBPF many-core architecture, a resource-optimized and highly configurable many-core architecture composed of custom VeBPF (Verilog eBPF) CPU cores designed for FPGA-based packet processing. The VeBPF cores are eBPF ISA compliant and implemented in Verilog HDL for seamless integration with existing FPGA IP blocks and subsystems. The proposed many-core architecture enables parallel execution of multiple eBPF rules across multiple VeBPF cores, achieving low-latency packet processing. The architecture is fully parameterizable, allowing the number of VeBPF cores and eBPF rules to scale according to application requirements and available FPGA resources. eBPF rules can be dynamically updated at run time without requiring FPGA reconfiguration, enabling flexible and adaptive network processing. The design incorporates hardware and computer architecture optimizations that support deployment across a wide range of platforms, from low-end FPGA-based IoT devices to high-end FPGA-based SmartNICs. In addition, we present automated testing and simulation frameworks developed using open-source tools such as Python and Cocotb. The VeBPF cores, many-core architecture, control software libraries, and simulation infrastructure are released as open source to support further research in FPGA-based many-core systems, eBPF acceleration, SmartNICs, IoT, and network security.

cs.CE↗

arXiv2023

Unikernel Linux (UKL)

This paper presents Unikernel Linux (UKL), a path toward integrating unikernel optimization techniques in Linux, a general purpose operating system. UKL adds a configuration option to Linux allowing for a single, optimized process to link with the kernel directly, and run at supervisor privilege. This UKL process does not require application source code modification, only a re-link with our, slightly modified, Linux kernel and glibc. Unmodified applications show modest performance gains out of the box, and developers can further optimize applications for more significant gains (e.g. 26% throughput improvement for Redis). UKL retains support for co-running multiple user level processes capable of communicating with the UKL process using standard IPC. UKL preserves Linux's battle-tested codebase, community, and ecosystem of tools, applications, and hardware support. UKL runs both on bare-metal and virtual servers and supports multi-core execution. The changes to the Linux kernel are modest (1250 LOC).

cs.OS↗

arXiv2022

Relational Memory: Native In-Memory Accesses on Rows and Columns

Analytical database systems are typically designed to use a column-first data layout to access only the desired fields. On the other hand, storing data row-first works great for accessing, inserting, or updating entire rows. Transforming rows to columns at runtime is expensive, hence, many analytical systems ingest data in row-first form and transform it in the background to columns to facilitate future analytical queries. How will this design change if we can always efficiently access only the desired set of columns? To address this question, we present a radically new approach to data transformation from rows to columns. We build upon recent advancements in embedded platforms with re-programmable logic to design native in-memory access on rows and columns. Our approach, termed Relational Memory, relies on an FPGA- based accelerator that sits between the CPU and main memory and transparently transforms base data to any group of columns with minimal overhead at runtime. This design allows accessing any group of columns as if it already exists in memory. We implement and deploy Relational Memory in real hardware, and we show that we can access the desired columns up to 1.63x faster than accessing them from their row-wise counterpart, while matching the performance of a pure columnar access for low projectivity, and outperforming it by up to 1.87x as projectivity (and tuple re-construction cost) increases. Moreover, our approach can be easily extended to support offloading of a number of operations to hardware, e.g., selection, group by, aggregation, and joins, having the potential to vastly simplify the software logic and accelerate the query execution.

cs.DB↗