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Selected Publications

• From JVM to GPU: Running LLMs natively in Java

  • M. Papadimitriou

  A feature article in JavaPro’s Java in the Age of AI issue on running large language models directly on GPUs from Java, using TornadoVM and GPULlama3.java.

• Scaling Up Performance of Managed Applications on NUMA Systems

  • O. Papadakis
  • A. Andronikakis
  • N. Foutris
  • M. Papadimitriou
  • A. Stratikopoulos
  • F. Zakkak
  • P. Xekalakis
  • C. Kotselidis

  Scaling up the performance of managed applications on NonUniform Memory Access (NUMA) architectures has been a challenging task, as it requires a good understanding of the underlying architecture and managed runtime environments(MRE). Prior work has studied this problem from the scope of specific components of the managed runtimes, such as the Garbage Collectors, as a means to increase the NUMA awareness in MREs. In this paper, we follow a different approach that complements prior work by studying the behavior of managed applications on NUMA architectures during mutation time. At first, we perform a characterization study that classifies several Dacapo and Renaissance applications as per their scalability-critical properties. Based on this study, we propose a novel lightweight mechanism in MREs for optimizing the scalability of managed applications on NUMA systems, in an application-agnostic way. Our experimental results show that the proposed mechanism can result in relative performance ranging from 0.66x up to 3.29x, with a geometric mean of 1.11x, against a NUMA-agnostic execution

• A Multifaceted Memory Analysis of Java Benchmarks

  • O. Papadakis
  • A. Andronikakis
  • N. Foutris
  • M. Papadimitriou
  • A. Stratikopoulos
  • F. Zakkak
  • P. Xekalakis
  • C. Kotselidis

  Java benchmarking suites like Dacapo and Renaissance are employed by the research community to evaluate the performance of novel features in managed runtime systems. These suites encompass various applications with diverse behaviors in order to stress test different subsystems of a managed runtime. Therefore, understanding and characterizing the behavior of these benchmarks is important when trying to interpret experimental results. This paper presents an in-depth study of the memory behavior of 30 Dacapo and Renaissance applications. To realize the study, a characterization methodology based on a two-faceted profiling process of the Java applications is employed. The two-faceted profiling offers comprehensive insights into the memory behavior of Java applications, as it is composed of high-level and low-level metrics obtained through a Java object profiler (NUMAProfiler) and a microarchitectural event profiler (PerfUtil) of MaxineVM, respectively. By using this profiling methodology we classify the Dacapo and Renaissance applications regarding their intensity in object allocations, object accesses, LLC, and main memory pressure. In addition, several other aspects such as the JVM impact on the memory behavior of the application are discussed.

• Multiple-Tasks on Multiple-Devices (MTMD): Exploiting Concurrency in Heterogeneous Managed Runtimes

  • M. Papadimitriou
  • E. Markou
  • J. Fumero
  • F. Blanaru
  • A. Stratikopoulos
  • C. Kotselidis

  In this work, we propose a novel approach for enabling a Java-based heterogeneous managed runtime to automatically and efficiently deploy multiple tasks on multiple devices. We extend TornadoVM with parallel execution of bytecode interpreters to dynamically and concurrently manage and execute arbitrary tasks across multiple OpenCL-compatible devices. In addition, in order to achieve an efficient devicetask allocation, we employ a machine learning approach with a multiple-classification architecture of Extra-Trees-Classifiers. Our proposed solution has been evaluated over a suite of 12 applications split into three different groups. Our experimental results showcase performance improvements up 83% compared to all tasks running on the single best device, while reaching up to 91% of the oracle performance.

• Automatically Exploiting the Memory Hierarchy of GPUs through Just-in-Time Compilation

  • M. Papadimitriou
  • J. Fumero
  • A. Stratikopoulos
  • C. Kotselidis

  In this work, we propose a novel approach for enabling a Java-based heterogeneous managed runtime to automatically and efficiently deploy multiple tasks on multiple devices. We extend TornadoVM with parallel execution of bytecode interpreters to dynamically and concurrently manage and execute arbitrary tasks across multiple OpenCL-compatible devices. In addition, in order to achieve an efficient devicetask allocation, we employ a machine learning approach with a multiple-classification architecture of Extra-Trees-Classifiers. Our proposed solution has been evaluated over a suite of 12 applications split into three different groups. Our experimental results showcase performance improvements up 83% compared to all tasks running on the single best device, while reaching up to 91% of the oracle performance.

• Transparent Compiler and Runtime Specializations for Accelerating Managed Languages on FPGAs

  • M. Papadimitriou
  • J. Fumero
  • A. Stratikopoulos
  • C. Kotselidis

  In recent years, heterogeneous computing has emerged as the vital way to increase computers’ performance and energy efficiency by combining diverse hardware devices, such as Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs). The rationale behind this trend is that different parts of an application can be offloaded from the main CPU to diverse devices, which can efficiently execute these parts as co-processors. FPGAs are a subset of the most widely used co-processors, typically used for accelerating specific workloads due to their flexible hardware and energy-efficient characteristics. These characteristics have made them prevalent in a broad spectrum of computing systems ranging from low-power embedded systems to high-end data centers and cloud infrastructures.

• Efficient compilation and execution of JVM-based data processing frameworks on heterogeneous co-processors

  • C. Kotselidis
  • Et. al.

  This paper addresses the fundamental question of how modern Big Data frameworks can dynamically and transparently exploit heterogeneous hardware accelerators. After presenting the major challenges that have to be addressed towards this goal, we describe our proposed architecture for automatic and transparent hardware acceleration of Big Data frameworks and applications. Our vision is to retain the uniform programming model of Big Data frameworks and enable automatic, dynamic Just-In-Time compilation of the candidate code segments that benefit from hardware acceleration to the corresponding format. In conjunction with machine learning-based device selection, that respect user-defined constraints (e.g., cost, time, etc.), we enable dynamic code execution on GPUs and FPGAs transparently to the user.

• Heterogeneous Computing Architectures: Challenges and Vision

  Heterogeneous Computing Architectures: Challenges and Vision provides an updated vision of the state-of-the-art of heterogeneous computing systems, covering all the aspects related to their design: from the architecture and programming models to hardware/software integration and orchestration to real-time and security requirements. The transitions from multicore processors, GPU computing, and Cloud computing are not separate trends, but aspects of a single trend-mainstream; computers from desktop to smartphones are being permanently transformed into heterogeneous supercomputer clusters. The reader will get an organic perspective of modern heterogeneous systems and their future evolution.

• Dynamic application reconfiguration on heterogeneous hardware

  • J. Fumero
  • M. Papadimitriou
  • F. Zakkak
  • M. Xekalaki
  • J. Clarkson
  • C. Kotselidis

  By utilizing diverse heterogeneous hardware resources, developers can significantly improve the performance of their applications. Currently, in order to determine which parts of an application suit a particular type of hardware accelerator better, an offline analysis that uses a priori knowledge of the target hardware configuration is necessary. To make matters worse, the above process has to be repeated every time the application or the hardware configuration changes.

• Towards Prototyping and Acceleration of Java Programs onto Intel FPGAs

  • M. Papadimitriou
  • J. Fumero
  • A. Stratikopoulos
  • C. Kotselidis

  In this work, we propose an approach for transparent compilation and execution of Java programs onto Intel FPGA devices. In detail, we showcase how a managed runtime environment can leverage Intel OpenCL SDK to generate specialized FPGA code, enabling prototyping and acceleration of Java Programs onto FPGAs. Finally, we describe our implementation in the context of TornadoVM with a clear objective to ease FPGA programmability allowing integration with existing frameworks.