As hardware complexity increases, profiling becomes essential for understanding system behavior.This paper compares different x86 sampling implementations for memory access profiling, revealing their complementary capabilities and limitations.Plus, we demonstrate that current abstractions like the perf subsystem inadequately expose platform-specific features.

Software-based prefetching is a powerful method for tolerating access penalties that are encountered by data processing systems: memory latency. Although the idea appears straightforward---simply informing the CPU about upcoming data accesses---the intricacies of its implementation remain insufficiently understood. Existing works demonstrate how to rewrite algorithms for prefetching, yet they often overlook the limitations and hardware implications of bringing data into the cache hierarchy. In this paper, we examine software-based prefetching thoroughly by delving into its implementation and identifying pitfalls across various platforms. Furthermore, we provide actionable insights and recommendations for developers seeking to boost their applications through this technique.

Modern hardware’s complexity has made studying hardware-conscious algorithms a relevant topic for many years. Partitioning algorithms, for instance, break data into bits that fit into fast CPU caches. Unfortunately, they are often challenging to design, develop, and maintain. While hardware-oblivious algorithms are easier to build, they may perform poorly when hardware or data deviate from expectations.

Abstract Query compilation is a processing technique that achieves very high processing speeds but has the disadvantage of introducing additional compilation latencies. These latencies cause an overhead that is relatively high for short-running and high-complexity queries. In this work, we present Flounder IR and ReSQL, our new approach to query compilation. Instead of using a general purpose intermediate representation (e.g., LLVM IR) during compilation, ReSQL uses Flounder IR, which is specifically designed for database processing. Flounder IR is lightweight and close to machine assembly. This simplifies the translation from IR to machine code, which otherwise is a costly translation step. Despite simple translation, compiled queries still benefit from the high processing speeds of the query compilation technique. We analyze the performance of our approach with micro-benchmarks and with ReSQL, which employs a full translation stack from SQL to machine code. We show reductions in compilation times up to two orders of magnitude over LLVM and show improvements in overall execution time for TPC-H queries up to 5.5 $$\times $$ × over state-of-the-art systems.

The hardware environment has changed rapidly in recent years: Many cores, multiple sockets, and large amounts of main memory have become a commodity. To benefit from these highly parallel systems, the software has to be adapted. Sophisticated latch-free data structures and algorithms are often meant to address the situation. But they are cumbersome to develop and may still not provide the desired scalability.

Query compilation can make query execution extremely efficient, but it introduces additional compilation time. The compilation time causes a relatively high overhead especially for short-running and high-complexity queries.