Related papers: Pheet meets C++11
Parameter-efficient transfer learning (PETL) based on large-scale pre-trained foundation models has achieved great success in various downstream applications. Existing tuning methods, such as prompt, prefix, and adapter, perform…
Parameter-Efficient Fine-Tuning (PEFT) methods achieve performance comparable to Full Fine-Tuning (FFT) while requiring significantly fewer computing resources, making it the go-to choice for researchers. We find that although PEFT can…
Rust is a relatively new system programming language that has been experiencing a rapid adoption in the past 10 years. Rust incorporates a memory ownership model enforced at a compile time. Since this model involves zero runtime overhead,…
In this paper the performance of INET's emulation mode is evaluated. In particular, the focus of the study is on the precision of the delay emulation. It is shown, that this precision is low in INET 2.6 (respectively a later version…
Temporal prefetching shows promise for handling irregular memory access patterns, which are common in data-dependent and pointer-based data structures. Recent studies introduced on-chip metadata storage to reduce the memory traffic caused…
Conventional heterogeneous computing systems built on PCIe interconnects suffer from inefficient fine-grained host-device interactions and complex programming models. In recent years, many proprietary and open cache-coherent interconnect…
In concurrent data structures, the efficiency of set operations can vary significantly depending on the workload characteristics. Numerous concurrent set implementations are optimized and fine-tuned to excel in scenarios characterized by…
For several decades, the CPU has been the standard model to use in the majority of computing. While the CPU does excel in some areas, heterogeneous computing, such as reconfigurable hardware, is showing increasing potential in areas like…
The field of parallel network simulation frameworks is evolving at a great pace. That is also because of the growth of Intelligent Transportation Systems (ITS) and the necessity for cost-effective large-scale trials. In this contribution,…
This paper presents a new tool to perform various steps in jet tagger development in an efficient and comprehensive way. A common data structure is used for training, as well as for performance evaluation in data. The introduction of this…
We present a federated, asynchronous, memory-limited algorithm for online task scheduling across large-scale networks of hundreds of workers. This is achieved through recent advancements in federated edge computing that unlocks the ability…
The Partitioned Global Address Space memory model has been popularised by a number of languages and applications. However this abstraction can often result in the programmer having to rely on some in built choices and with this implicit…
This paper describes a C++ library that compiles neural network models at runtime into machine code that performs inference. This approach in general promises to achieve the best performance possible since it is able to integrate statically…
Tuning parallel file system in High-Performance Computing (HPC) systems remains challenging due to the complex I/O paths, diverse I/O patterns, and dynamic system conditions. While existing autotuning frameworks have shown promising results…
C++ Modules come in C++20 to fix the long-standing build scalability problems in the language. They provide an io-efficient, on-disk representation capable to reduce build times and peak memory usage. ROOT employs the C++ modules technology…
Adapting large, object-oriented C++ codebases for hardware acceleration might be extremely challenging, particularly when targeting heterogeneous platforms such as GPUs. Marionette is a C++17 library designed to address this by enabling…
Performance has always been a hot topic in computing. However, the viable ways to achieve it have taken many forms in the different moments of computing history. Today, technological limits have pushed the adoption of increasingly parallel…
Since the advent of parallel algorithms in the C++17 Standard Template Library (STL), the STL has become a viable framework for creating performance-portable applications. Given multiple existing implementations of the parallel algorithms,…
Training machine learning models requires feeding input data for models to ingest. Input pipelines for machine learning jobs are often challenging to implement efficiently as they require reading large volumes of data, applying complex…
Different bitstring representations can yield varying computational performance. This work compares three bitstring implementations in C++: std::bitset, boost::dynamic_bitset, and a custom direct implementation. Their performance is…