Will X. Y. Li, Rosa H. M. Chan, Wei Zhang, Chiwai Yu, Dong Song, Theodore W. Berger et al. Pages PDF ยท High-Performance FPGA-Accelerated. High-Performance Computing using FPGA covers the area of high performance Included format: EPUB, PDF; ebooks can be used on all reading devices. Request PDF on ResearchGate | High-performance computing using FPGAs | High-Performance Computing using FPGA covers the area of high performance.

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FPGAs have historically been restricted to a narrow set of HPC applications because of . accelerated using in-socket FPGA accelerators. In-socket .. http:// phisrebiberkotch.gq 2. HIGH PERFORMANCE SCIENTIFIC COMPUTING USING FPGAS WITH IEEE. FLOATING POINT possible to implement high-performance matrix and vector kernel operations on .. available at phisrebiberkotch.gq High-Performance Reconfigurable Computers are parallel computing systems that contain multiple settings, the design uses FPGAs as coprocessors that.

Implementations on sequential processors typically rotate the molecule in a step separate from the correlation. The preferred technique is based on runtime index calculation and has two distinctive features. First, index computation can be pipelined to generate indices at operating frequency due to the predictable order of access to voxels.

High-Performance Computing Using FPGAs

Method 6:Use rate-matching to remove bottlenecks Computations often consist of independent function sequences, such as a signal passing through a series of filters and transformations. Multiprocessor implementations offer some flexibility in partitioning by function or data, but on an FPGA, functions are necessarily laid out on the chip and so function-level parallelism is built in although functions can also be replicated for data parallelism.

This implies pipelining not only within, but also across, functions.

Application example DNA microarrays simultaneously measure the expression of tens of thousands of genes, and are used to investigate numerous questions in biology. One approach is to analyze on the order of a hundred samples, each with tens of thousands of gene expressions, to find correlations between expression patterns and disease phenomena.

The kernel operation is a series of dot-product and sum DPS calculations feeding covariance, matrix inversion, and regression CIR logic.

High-Performance Computing Using FPGAs

Usually the solution involves a very deep pipeline hundreds or even thousands of stages long. Difficulty arises, however, when successive functions have different rates of sourcing and sinking data. The solution is to rate-match sequential functions by replicating the slower functions and then using them in rotation for the desired throughput.

In the past five years, however, an ever larger fraction of their chip area has been devoted to hard-wired components, such as integer multipliers and independently accessible BRAMs. For example, the Xilinx VP has independently addressable, bit, quad-ported BRAMs; it achieves a sustained bandwidth of 20 terabytes per second at capacity.

Using this bandwidth greatly facilitates high performance and is an outstanding asset of current-generation FPGAs. Application example In molecular dynamics, efficient algorithms for computing the electrostatic interaction often involve mapping charges onto a 3D grid.

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The first phase of each iteration computes the 3D charge distribution, while the second phase locates each atom in that field and applies a force to it according to its charges and that region of the force field. Because atoms almost never align to the grid points on which the field is computed, trilinear interpolation uses the eight grid points nearest to the atom to determine field strength. Key to such a structure is simultaneous access to all grid points surrounding the atom.

This in turn requires appropriate partitioning of the 3D grid among the BRAMs to enable collisionless access, and also efficient logic to convert atom positions into BRAM addresses. We have prototyped a memory-access configuration that supports tricubic interpolation by fetching 64 neighboring grid-point values per cycle.

We have also generalized this technique into a tool that creates custom interleaved memories for access kernels of various sizes, shapes, and dimensionality. Method 8:Use appropriate arithmetic precision With high-end microprocessors having bit data paths, often overlooked is that many BCB applications require only a few bits of precision.

In fact, even the canonical floating point of MD is often implemented with substantially reduced precision, although this remains controversial. In contrast with microprocessors, FPGAs enable configuration of data paths into arbitrary sizes, allowing a tradeoff between precision and parallelism. An additional benefit of minimizing precision comes from shorter propagation delays through narrower arithmetic units.

Application example All BCB applications described here benefit substantially from the selection of nonstandard data type sizes. For example, microarray values and biological sequences require only two to five bits, and shape characterization of a rigid molecule requires only two to seven bits. While most MD applications require more than the 24 bits provided by a single-precision floating point, they might not need double precision 53 bits.

That study examined six different models describing intermolecular forces.

Molecule descriptions range from two to seven bits per voxel, and scoring functions varied with the application. The number of PEs that fit the various maximum-sized cubical computing arrays into a Xilinx XC2VP70 ranged from 83 to 2, , according to the resources each PE needed.

Since clock speeds also differed for each application-specific accelerator, they covered a performance range. If we had been restricted to, for example, 8-bit arithmetic, the performance differential would have been even greater.

Method 9:Use appropriate arithmetic mode Microprocessors provide support for integers and floating point, and, depending on multimedia features, 8-bit saturated values. In digital signal processing systems, however, cost concerns often require DSPs to have only integers.

Software can emulate floating point when required; also common is use of block floating point.

Alternatives include the block floating point, log representations, and the semi-floating point. We would generally use double-precision floating points for further computations. This enables the use of a stripped-down floating-point mode, particularly one that does not require a variable shift. The resulting force pipelines with bit precision are 25 percent smaller than ones built with a commercial single-precision bit floating-point library.

Achieving High Performance with FPGA-Based Computing

Method Minimize use of high-cost arithmetic operations The relative costs of arithmetic functions are different on FPGAs than on microprocessors.

For example, FPGA integer multiplication is efficient compared to addition, while division is orders-of-magnitude slower. Even if the division logic is fully pipelined to hide its latency, the cost remains high in chip area, especially if the logic must be replicated. Thus, restructuring arithmetic with respect to an FPGA cost function can substantially increase performance. Application example The microarray data analysis kernel as originally formulated requires division.

This doubles the required number of bits, but rational values are needed only at a short, late segment of the data path.

Consequently, the additional logic required for the wider data path is far lower than the logic for division would have been.

These methods differ from the others in that they require design tools not widely in use, either because they are currently proprietary 11 or exist only as prototypes. Furthermore, by replicating an algorithm on an FPGA or the use of a multiplicity of FPGAs has enabled reconfigurable SIMD systems to be produced where several computational devices can concurrently operate on different data, which is highly parallel computing.

This heterogeneous systems technique is used in computing research and especially in supercomputing. Field programmable gate arrays are often used as a support to partial reconfiguration. Electronic hardware , like software , can be designed modularly, by creating subcomponents and then higher-level components to instantiate them. In many cases it is useful to be able to swap out one or several of these subcomponents while the FPGA is still operating.

Normally, reconfiguring an FPGA requires it to be held in reset while an external controller reloads a design onto it. Partial reconfiguration allows for critical parts of the design to continue operating while a controller either on the FPGA or off of it loads a partial design into a reconfigurable module. Partial reconfiguration also can be used to save space for multiple designs by only storing the partial designs that change between designs. A common example for when partial reconfiguration would be useful is the case of a communication device.

High-performance computing using FPGAs

If the device is controlling multiple connections, some of which require encryption , it would be useful to be able to load different encryption cores without bringing the whole controller down. Partial reconfiguration is not supported on all FPGAs.

A special software flow with emphasis on modular design is required.As a technology, audio, and gadget enthusiast his entire life, Rob also writes for TONEAudio Magazine, reviewing high-end home audio equipment. Method 3: Once the programmer is satisfied with the current partitioned code the hardware generator is used to generate VHDL code for the FPGA fabric targeted functions. Further distressing was the fact that these modifications were not being made because of limitations in the CHiMPS toolset but rather in the choice of available FHPC platform for execution.

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