The global phenomenon of forest degradation is a pressing issue with severe implications for climate stability and biodiversity protection. In this work we generate Bayesian updating deforestation detection (BUDD) algorithms by incorporating Sentinel-l backscatter and interferometric coherence with Sentinel-2 normalized vegetation index data. We show that the algorithm provides good performance in validation AOIs. We compare the effectiveness of different combinations of the three data modalities as inputs into the BUDD algorithm and compare against existing benchmarks based on optical imagery.

We consider the problem of unwrapping the phase of two-dimensional interferograms, and adopt a known formulation as a sparse optimization problem. Many algorithms have been developed for solving sparse optimization problems that occur in the field of compressive sensing; in this work, we adapt one such algorithm for use in the unwrapping problem. The result is an unwrapping algorithm that gives very similar results to those of existing algorithms, but that is simpler, more reliable, and more computationally efficient.

We report on improvements made over the past two decades to our adaptive treecode N-body method (HOT). A mathematical and computational approach to the cosmological N-body problem is described, with performance and scalability measured up to 256k (218) processors. We present error analysis and scientific application results from a series of more than ten 69 billion (40963) particle cosmological simulations, accounting for 4 x 1020 floating point operations. These results include the first simulations using the new constraints on the standard model of cosmology from the Planck satellite. Our simulations set a new standard for accuracy and scientific throughput, while meeting or exceeding the computational efficiency of the latest generation of hybrid TreePM N-body methods.

The Space Simulator is a 294-processor Beowulf cluster with theoretical peak performance just below 1.5 Teraflop/s. It is based on the Shuttle XPC SS51G mini chassis. Each node consists of a 2.53 GHz Pentium 4 processor, 1 Gb of 333 MHz DDR SDRAM, an 80 Gbyte Maxtor hard drive, and a 3Com 3C996B-T gigabit ethernet card. The network is made up of a Foundry FastIron 1500 and 800 Gigabit Ethernet switch. Each individual node cost less than $1000, and the entire system cost under $500,000. The cluster achieved Linpack performance of 665.1 Gflop/s on 288 processors in October 2002, making it the 85th fastest computer in the world according to the 20th TOP500 list. Performance has since improved to 757.1 Linpack Gflop/s, ranking at #88 on the 21st TOP500 list. This is the first machine in the TOP500 to surpass Linpack price/performance of 1 dollar per Mflop/s.

We present a new twist to the Beowulf cluster - the Bladed Beowulf. In contrast to traditional Beowulfs which typically use Intel or AMD processors, our Bladed Beowulf uses Trans-meta processors in order to keep thermal power dissipation low and reliability and density high while still achieving comparable performance to Intel- and AMD-based clusters. Given the ever increasing complexity of traditional supercomputers and Beowulf clusters; the issues of size, reliability power consumption, and ease of administration and use will be "the" issues of this decade for high-performance computing. Bigger and faster machines are simply not good enough anymore. To illustrate, we present the results of performance benchmarks on our Bladed Beowulf and introduce two performance metrics that contribute to the total cost of ownership (TCO) of a computing system - performance/power and performance/space.

In this paper, we present a novel twist on the Beowulf cluster - the Bladed Beowulf. Designed by RLX Technologies and integrated and configured at Los Alamos National Laboratory, our Bladed Beowulf consists of compute nodes made from commodity off-the-shelf parts mounted on motherboard blades measuring 14.7" /spl times/ 4.7" /spl times/ 0.58". Each motherboard blade (node) contains a 633 MHz Trans-meta TM5600/spl trade/ CPU, 256 MB memory, 10 GB hard disk, and three 100-Mb/s Fast Ethernet network interfaces. Using a chassis provided by RLX, twenty-four such nodes mount side-by-side in a vertical orientation to fit in a rack-mountable 3U space, i.e., 19" in width and 5.25" in height. A Bladed Beowulf can reduce the total cost of ownership (TCO) of a traditional Beowulf by a factor of three while providing Beowulf-like performance. Accordingly, rather than use the traditional definition of price-performance ratio where price is the cost of acquisition, we introduce a new metric called ToPPeR: total price-performance ratio, where total price encompasses TCO. We also propose two related (but more concrete) metrics: performance-space ratio and performance-power ratio.

We present results from computations on Green Destiny, a 240-processor Beowulf cluster which is contained entirely within a single 19-inch wide 42U rack. The cluster consists of 240 Transmeta TM5600 667-MHz CPUs mounted on RLX Technologies motherboard blades. The blades are mounted side-by-side in an RLX 3U rack-mount chassis, which holds 24 blades. The overall cluster contains 10 chassis and associated Fast and Gigabit Ethernet switches. The system has a footprint of 0.5 meter 2 (6 square feet), a volume of 0.85 meter 3 (30 cubic feet) and a measured power dissipation under load of 5200 watts (including network switches). We have measured the performance of the cluster using a gravitational treecode N-body simulation of galaxy formation using 200 million particles, which sustained an average of 38.9 Gflops on 212 nodes of the system. We also present results from a three-dimensional hydrodynamic simulation of a core-collapse supernova.

We report on an eficient adaptive N-body method which u~e have recently designed and implemented.The algorithm computes the forces on an arbitrary distribution of bodies in a time which scales as N log N with the particle number.The acclwacy of the force calculations is analytically bounded, and can be adjusted via a user dejined parameter be fit'een a few percent relative accaracy, doivn to machine arithmetic accuracy.Instead of using pointers to indicate the topology of the tree, we identify each possible cell with a key.The mapping of keys into memory locations is achieved via a hash table.This allows the program to access data in an eflcient manner across multiple processors.Performance of the parallel program is measured on the 512 processor Intel Touchstone Delta system.We also comment on a number of wide-ranging applications which can benejitfiom application of this type of algorithm.1 Introduction N-body simulations have become a fundamental tool in the study of complex physical systems.Starting fkom a basic physical interaction (e.g., gravitational, Coulombic, Biot-Savart, van der Waals) one can follow the dynamical evolution of a system of N bodies, which represent the phase-space density distribution of the system.N-body simulations are essentially statistical in nature (unless the physical system can be directly modeled by N bodies, as is the case in some molectdar dynamics simulations).More bodies implies a more accurate and complete sampling of the phase space, and hence more accurate or complete results.Unfortunately, the minimum aeeuracy required to model systems of interest often depends on having N be much larger than current computational resources allow.Because interactions occur between each pair of particles in a N-body simulation, the computational work scales

The authors report on recent large astrophysical N-body simulations executed on the Intel Touchstone Delta system. They review the astrophysical motivation and the numerical techniques and discuss steps taken to parallelize these simulations. The methods scale as O(N log N), for large values of N, and also scale linearly with the number of processors. The performance sustained for a duration of 67 h, was between 5.1 and 5.4 Gflop/s on a 512-processor system.>