TreadMarks supports parallel computing on networks of workstations by providing the application with a shared memory abstraction. Shared memory facilitates the transition from sequential to parallel programs. After identifying possible sources of parallelism in the code, most of the data structures

TreadMarks is a distributed shared memory (DSM) system for standard Unix systems such as SunOS and Ultrix. This paper presents a performance evaluation of TreadMarks running on Ultrix using DECstation-5000/240's that are connected by a 100-Mbps switch-based ATM LAN and a 10-Mbps Ethernet

of workstations. This report studies how to support heterogeneity in a particular DSM system developed at Rice University called TreadMarks. We modified TreadMarks in two steps : first we adjusted TreadMarks for networks made up of machines with the same byte ordering and then we studied the case of different

TreadMarks supports parallel computing on networks of workstations by providing the application with a shared memory abstraction. Shared memory facilitates the transition from sequential to parallel programs. After identifying possible sources of parallelism in the code, most of the data structures

applications, moldyn and nbf, using the Tread-Marks DSM system on an 8-processor IBM SP2. We find that it has similar performance to the inspector-executor method supported by the CHAOS run-time library, while requiring much simpler compile-time support. For moldyn, it is up to 23~0 faster than CHAOS

We compare the performance of software-supported shared memory on a general-purpose network to hardware-supported shared memory on a dedicated interconnect. Up to eight processors, our results are based on the execution of a set of application programs on a SGI 4D/480 multiprocessor and on TreadMarks

Irregular reductions form the core of adaptive irregular codes. On distributed-memory multiprocessors, they are parallelized either using sophisticated run-time systems (e.g., CHAOS, PILAR) or the shared-memory interface supported by software DSMs (e.g., CVM, TreadMarks). We introduce LOCALWRITE, a

Compilers for distributed-memory multiprocessors parallelize irregular reductions either by generating calls to sophisticated run-time systems or relying on the sharedmemory interface supported by software DSMs. Run-time systems gather/scatter nonlocal results (e.g., CHAOS, PI-LAR) while software

. We present results for implementations of our fault-tolerant technique using simulations of both TreadMarks, a software-only DSM, and Cashmere, a DSM using memory mapped hardware. We compare simulations with no fault tolerance to simulations with local disk logging and peer logging. We present

We present a new run-time system for typed programming languages that supports object sharing in a distributed system. The key insight in this system is that the ability to distinguish pointers from data at run-time enables efficient and transparent sharing of data with both fine-grained and coarse