You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Trying to cope with the constantly growing number of cores per processor, hardware architects are experimenting with modular non-cache-coherent architectures. Such architectures delegate the memory coherency to the software. On the contrary, high productivity languages, like Java, are designed to abstract away the hardware details and allow developers to focus on the implementation of their algorithm. Such programming languages rely on a process virtual machine to perform the necessary operations to implement the corresponding memory model. Arguing about the correctness of such implementations is not trivial though. In this work we present our implementation of the Java Memory Model in a Java Virtual Machine targeting a 512-core non-cache-coherent memory architecture. We shortly discuss design decisions and present early evaluation results, which demonstrate that our implementation scales with the number of cores. We model our implementation as the operational semantics of a Java Core Calculus that we extend with synchronization actions, and prove its adherence to the Java Memory Model.

Cited Abstract(s):
[1]: Heterogeneous multi-core processors, such as the IBM Cell processor, can deliver high performance. However, these processors are notoriously difficult to program: different cores support different instruction set architectures, and the processor as a whole does not provide coherence between the different cores' local memories. We present Hera-JVM, an implementation of the Java Virtual Machine which operates over the Cell processor, thereby making this platforms more readily accessible to mainstream developers. Hera-JVM supports the full Java language; threads from an unmodified Java application can be simultaneously executed on both the main PowerPC-based core and on the additional SPE accelerator cores. Migration of threads between these cores is transparent from the point of view of the application, requiring no modification to Java source code or bytecode. Hera-JVM supports the existing Java Memory Model, even though the underlying hardware does not provide cache coherence between the different core types. We examine Hera-JVM's performance under a series of real-world Java benchmarks from the SpecJVM, Java Grande and Dacapo benchmark suites. These benchmarks show a wide variation in relative performance on the different core types of the Cell processor, depending upon the nature of their workload. Execution of these benchmarks on Hera-JVM can achieve speedups of up to 2.25x by using one of the Cell processor's SPE accelerator cores, compared to execution on the main PowerPC-based core. When all six SPE cores are exploited, parallel workloads can achieve speedups of up to 13x compared to execution on the single PowerPC core.

[2]: 

[3]: Current developments in microprocessor design favor increased core counts over frequency scaling to improve processor performance and energy efficiency. Coupling this architectural trend with a message-passing protocol helps realize a data-center-on-a-die. The prototype chip (Figs. 5.7.1 and 5.7.7) described in this paper integrates 48 Pentium™ class IA-32 cores [1] on a 6×4 2D-mesh network of tiled core clusters with high-speed I Os on the periphery. The chip contains 1.3B transistors. Each core has a private 256KB L2 cache (12MB total on-die) and is optimized to support a message-passing-programming model whereby cores communicate through shared memory. A 16KB message-passing buffer (MPB) is present in every tile, giving a total of 384KB on-die shared memory, for increased performance. Power is kept at a minimum by transmitting dynamic, fine-grained voltage-change commands over the network to an on-die voltage-regulator controller (VRC). Further power savings are achieved through active frequency scaling at the tile granularity. Memory accesses are distributed over four on-die DDR3 controllers for an aggregate peak memory bandwidth of 21GB s at 4× burst. Additionally, an 8-byte bidirectional system interface (SIF) provides 6.4GB s of I O bandwidth. The die area is 567mm2 and is implemented in 45nm high-к met al-gate CMOS [2].
To the best of our knowledge, the only other JVM implementing the Java memory model on a non cache coherent architecture is Hera-JVM [1] . Hera-JVM also employs caches which it handles in a similar manner to our implementation, with the difference that it starts a write-back at every write, as we discuss in sec:implementation . Regarding the synchronization mechanisms, Hera-JVM relies on the Cell B.E.'s GETLLAR and PUTLLC instructions to build an atomic compare-and-swap operation. However, such instructions are not available on the architectures at hand [3] [2] . Additionally, Hera-JVM did not aim to formally prove its adherence to the JMM.